An ac-dc hybrid power distribution network reconstruction planning method and system

Abstract

The application discloses a reconstruction planning method and system for an AC-DC hybrid power distribution network, which comprises the following steps: constructing a multi-layer model based on the reconstruction project type of the AC-DC hybrid power distribution network; solving the multi-layer model from bottom to top based on a preset algorithm to obtain the capacity of a distributed power supply and a voltage source converter; and reconstructing the AC-DC hybrid power distribution network based on the capacity of the distributed power supply and the voltage source converter. The multi-layer model comprises a lower-layer model constructed based on network optimization, a middle-layer model constructed based on distributed power supply planning and an upper-layer model constructed based on DC reconstruction planning. In the application, the lower-layer model, the middle-layer model and the upper-layer model are constructed, the capacity planning independence of the distributed power supply and the voltage source converter is considered, reasonable capacity planning results are obtained, reasonable schemes are provided for actual projects, and the application has universality for various reconstruction scenes. In the application, the multi-layer model is solved from bottom to top, and the accuracy and the calculation efficiency of the solution model are improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid transformation, and in particular to a method and system for planning the transformation of an AC / DC hybrid distribution network. Background Art

[0002] Distribution network transformation planning is a very realistic issue in the actual operation of the distribution network. The overall implementation of the transformation plan needs to consider multiple influencing factors, including reliability, economy, etc. Selecting the most reasonable plan requires a high degree of rationality in establishing the selected plan model.

[0003] On the one hand, as existing loads continue to grow, the carrying capacity of traditional AC distribution networks is gradually reaching its limits. Traditional methods of transformation, such as replacing equipment, require replacing all network equipment, which is economically prohibitive. On the other hand, with the recent rapid development of DC loads such as electric vehicles and renewable, clean, and efficient distributed power sources such as photovoltaics, the approach to transforming traditional power grids is bound to change.

[0004] The above problems can be solved through DC transformation. However, since the existing industrial loads basically all use AC motors, transforming the entire distribution network into a DC network requires all factory equipment to be updated, which is not realistic from the perspective of economy and feasibility. AC / DC hybrid distribution network is a major trend of future development, but there is currently no relevant research method for partial transformation of the distribution network, that is, sizing the voltage source converter station (VSC). At the same time, the site selection, sizing and optimized operation problems of distributed power sources are mostly in the field of AC distribution network, and the distributed power source sizing planning method for this new scenario of hybrid network has not been considered. Summary of the Invention

[0005] In order to solve the problem of lack of sizing planning for distributed power sources and voltage converters in the prior art, the present invention provides a method and system for planning the transformation of an AC / DC hybrid distribution network.

[0006] The technical solution provided by the present invention is:

[0007] A method for transforming an AC / DC hybrid distribution network, comprising:

[0008] Building a multi-layer model based on the AC / DC hybrid distribution network transformation project type;

[0009] Solving the multi-layer model from bottom to top based on a pre-set algorithm to obtain the capacity of the distributed power supply and the voltage source converter;

[0010] Transforming the AC / DC hybrid distribution network based on the capacity of the distributed power supply and the voltage source converter;

[0011] Among them, the types of transformation projects include: network optimization, distributed power planning and DC transformation planning;

[0012] The multi-layer model includes: a lower layer model constructed based on network optimization, a middle layer model constructed based on distributed power supply planning, and an upper layer model constructed based on DC transformation planning.

[0013] Preferably, the multi-layer model is solved from bottom to top based on a pre-set algorithm to obtain the capacity of the distributed power supply and the voltage source converter, including:

[0014] The active power value of the distributed generation is obtained by solving the lower model through enumeration method;

[0015] Based on the unit capacity and active power value of distributed power sources, the installation quantity of distributed power sources is obtained by solving the elite-retention genetic algorithm;

[0016] Based on the installation quantity and the unit capacity of the voltage source converter, the capacity of the voltage source converter is obtained by using a cone programming method.

[0017] Preferably, the lower layer model constructed based on network optimization includes:

[0018] Based on the active power output of distributed power sources, the objective function of the lower model is constructed;

[0019] The lower-level model constraints are set, and the lower-level model constraints include: available output constraints of distributed power sources, output constraints of distributed power sources, penetration constraints of single distributed power sources, total penetration constraints of distributed power sources, active and reactive balance constraints of power flow equations, node voltage constraints, and branch voltage constraints.

[0020] The distributed power supply includes: a generator set and a photovoltaic cell.

[0021] Preferably, the objective function is as follows:

[0022]

[0023] Where N represents the number of VSCs installed, C vsc represents the unit capacity investment cost of VSC, S vsc represents the installed capacity of VSC, a is the unit power operation cost, b is the maintenance cost coefficient of VSC, Δt is the time period length (1h in this invention); P i is the active power flowing out of VSC at the i-th moment, Z mid represents the optimal solution at the middle level, D represents the number of running days, and h represents the number of running hours.

[0024] Preferably, the middle-level model constructed based on distributed power generation planning includes:

[0025] Based on the capacity and active power value of distributed generation, the objective function of the middle-level model is constructed;

[0026] The middle-level model constraint conditions are set, wherein the middle-level model constraint conditions include: a maximum capacity constraint of a distributed power source, a unit capacity constraint of a distributed power source, and a total installed capacity constraint of a distributed power source.

[0027] Preferably, the objective function of the middle-level model is as follows:

[0028]

[0029] Where N represents the number of DG installations, C wdDG represents the unit capacity investment cost of wind turbine DG, S wdDG Indicates the installed capacity of wind turbine DG; C pvDG represents the unit capacity investment cost of photovoltaic cells DG, S pvDG represents the installed capacity of photovoltaic cells DG, Z down is the optimal solution for the lower model.

[0030] Preferably, the upper-level model constructed based on the DC transformation plan includes:

[0031] constructing an upper model objective function based on the unit capacity of the distributed power source and the voltage source converter;

[0032] The upper model constraint conditions are set, wherein the upper model constraint conditions include: a maximum capacity constraint of the voltage source converter, a unit capacity constraint of the voltage source converter, and a through power constraint of the voltage source converter.

[0033] Preferably, the upper model objective function is as follows:

[0034]

[0035] Where N represents the number of DG installations, C wdDG represents the unit capacity investment cost of wind turbine DG, S wdDG represents the installed capacity of wind turbine DG, C pvDG represents the unit capacity investment cost of photovoltaic cells DG, S pvDG represents the installed capacity of photovoltaic cells DG, P iwd is the active power generated by the distributed power supply of the wind turbine at the i-th moment, P ipv is the active power generated by the photovoltaic cell distributed power supply at the i-th moment, D represents the number of operating days, and h represents the operating hours.

[0036] An AC / DC hybrid distribution network transformation system, comprising:

[0037] Modeling module: building a multi-layer model based on the AC / DC hybrid distribution network transformation project type;

[0038] Solving module: solving the multi-layer model from bottom to top based on a pre-set algorithm to obtain the capacity of the distributed power supply and the voltage source converter;

[0039] Transformation module: transforming the AC / DC hybrid distribution network based on the capacity of the distributed power supply and the voltage source converter;

[0040] Among them, the types of transformation projects include: network optimization, distributed power planning and DC transformation planning;

[0041] The multi-layer model includes: a lower layer model constructed based on network optimization, a middle layer model constructed based on distributed power supply planning, and an upper layer model constructed based on DC transformation planning.

[0042] Preferably, the solution module includes:

[0043] The first solving submodule: solves the lower model by enumeration method to obtain the active power value of the distributed power supply;

[0044] The second solution submodule: Based on the unit capacity and active power value of the distributed power source, the installation quantity of the distributed power source is obtained through the elite retention genetic algorithm;

[0045] The third solving submodule: based on the installation quantity and the unit capacity of the voltage source converter, the capacity of the voltage source converter is solved by a cone programming method.

[0046] Preferably, the modeling module includes:

[0047] Lower-level objective function construction submodule: constructs the lower-level model objective function based on the active power output of the distributed power supply;

[0048] Lower-level constraint condition construction submodule: set the lower-level model constraint conditions to obtain the lower-level model;

[0049] The lower-level model constraints include: available output constraints of distributed power sources, output constraints of distributed power sources, penetration constraints of single distributed power sources, total penetration constraints of distributed power sources, active and reactive power balance constraints of power flow equations, node voltage constraints, and branch voltage constraints.

[0050] The distributed power supply includes: a generator set and a photovoltaic cell.

[0051] Preferably, the objective function constructed in the lower layer objective function construction submodule is as shown below:

[0052]

[0053] Where N represents the number of VSCs installed, C vsc represents the unit capacity investment cost of VSC, S vsc represents the installed capacity of VSC, a is the unit power operation cost, b is the maintenance cost coefficient of VSC, Δt is the time period length (1h in this invention); P i is the active power flowing out of VSC at the i-th moment, Z mid represents the optimal solution at the middle level, D represents the number of running days, and h represents the number of running hours.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The technical solution provided by the present invention includes: constructing a multi-layer model based on the type of AC / DC hybrid distribution network transformation project; solving the multi-layer model from the bottom up based on a pre-set algorithm to obtain the capacity of distributed power sources and voltage source converters; transforming the AC / DC hybrid distribution network based on the capacity of the distributed power sources and voltage source converters; wherein the transformation project types include: network optimization, distributed power source planning, and DC transformation planning; the multi-layer model includes: a lower-layer model constructed based on network optimization, a middle-layer model constructed based on distributed power source planning, and an upper-layer model constructed based on DC transformation planning. In this solution, by constructing the lower-layer model, the middle-layer model, and the upper-layer model, the capacity planning independence of the distributed power sources and voltage converters is taken into account, and a reasonable capacity planning result is obtained, which provides a reasonable solution for actual projects and is universal for various transformation scenarios.

[0056] In this solution, a bottom-up approach is adopted to solve the multi-layer model, which improves the accuracy and computational efficiency of the solution model. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a method for transforming an AC / DC hybrid distribution network according to the present invention;

[0058] Figure 2 A schematic diagram of the model interaction relationship in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of coding for an elite-retaining genetic algorithm according to an embodiment of the present invention;

[0060] Figure 4 4 is a flowchart of the overall solution in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to better understand the present invention, the present invention is further described below with reference to the accompanying drawings and examples.

[0062] Example 1:

[0063] This embodiment provides a method for planning the transformation of an AC / DC hybrid distribution network, as shown in the flowchart of the method. Figure 1 Shown, including:

[0064] S1: Construct a multi-layer model based on the AC / DC hybrid distribution network transformation project type.

[0065] 1: Build the upper model:

[0066] The upper-level model establishes an objective function to maximize the overall transformation benefits from a societal perspective, minimizing the overall investment minus the overall benefit. The upper-level optimization variables are the location and capacity of the VSCs.

[0067] The upper objective function is:

[0068]

[0069] Where: C inv represents the VSC equipment investment cost, C OP Indicates the VSC equipment operation and maintenance costs, ΔP std It represents the gross national product created by the additional load of the power grid after the overall transformation compared with that before the transformation, Z mid represents the middle-level objective function.

[0070] The expression of VSC equipment investment cost is:

[0071]

[0072] Where: N represents the number of VSCs installed, C vsc represents the unit capacity investment cost of VSC, S vsc Indicates the installed capacity of the VSC.

[0073] The expression of VSC equipment operation and maintenance cost is as follows:

[0074] C OP =C0+C m (3)

[0075]

[0076]

[0077] Where: C0 and C m are operating costs and maintenance costs respectively; N is the number of years; a is the operating cost per unit of electricity; Δt is the time period (1 hour is used in this invention); P i is the active power flowing out of the VSC at the i-th moment; b represents the maintenance cost coefficient of the VSC.

[0078] The gross national product created by the additional load on the power grid after the overall transformation compared to before the transformation is expressed as follows:

[0079] C ΔPstd =(E af -E bf )k GDP (6)

[0080] Where: E af It represents the total annual electricity consumption after the transformation, E bf Indicates the total annual electricity consumption if no transformation is done, k GDP Represents the correlation coefficient between electricity consumption and GDP.

[0081] Upper level constraints:

[0082] The capacity of the converter station VSC cannot be selected indefinitely. Due to physical factors and other reasons, the construction capacity of the VSC needs to be less than its maximum buildable capacity:

[0083] St

[0084] S VSC,i =m i S VSC,0 (7)

[0085]

[0086]

[0087] Where: S vsc，0 Indicates the rated capacity of VSC unit, m i is a non-negative integer, representing the VSC unit capacity coefficient installed on node i. =([i],[i]) represents the upper limit of the VSC capacity allowed to be installed at node i. PDC represents the load in the transformed DC line. NDC represents the number of loads in the DC line.

[0088] 2. Build a middle-level model:

[0089] The middle-level model establishes an objective function from the perspective of the whole society with the goal of maximizing the total benefits of installing distributed power sources, and the middle-level optimization variables are the location and capacity of distributed power sources.

[0090] Middle layer objective function:

[0091] minZ mid =C inv +Z down (10)

[0092] Where: C inv represents the investment cost of distributed power equipment, Z down represents the lower layer objective function.

[0093] The expression of the investment cost of distributed power supply equipment is:

[0094]

[0095] Where: N represents the number of DG installations, C wdDG represents the unit capacity investment cost of wind turbine DG, S wdDG Indicates the installed capacity of wind turbine DG; C pvDG represents the unit capacity investment cost of photovoltaic cells DG, S pvDG Represents the installed capacity of photovoltaic cells DG.

[0096] Middle-level constraints:

[0097] The capacity of distributed power generation DG cannot be selected without limit. Due to physical factors and other reasons, the construction capacity of distributed power generation DG needs to be less than its maximum constructible capacity. At the same time, most regions install distributed power generation with total capacity as the goal, so the total installed capacity is required to be a constant in the constraints.

[0098] St

[0099] S rated,i =a i S DG,0 (12)

[0100]

[0101]

[0102] Where: S rated,i is the installed capacity of DG at node i, is the upper limit of the DG capacity allowed to be installed on node i, S DG,0 is the rated capacity of DG unit; a i is a non-negative integer, representing the coefficient of the number of DGs installed on node i, S total Represents the total installed capacity of regional distributed power generation.

[0103] 3. Build the lower layer model:

[0104] The lower-level model establishes an objective function from the perspective of the whole society with the goal of maximizing the overall system operation benefits, and the lower-level optimization variables are the active output and reactive output of distributed power sources.

[0105] Lower layer objective function:

[0106]

[0107] Where: C op represents the operation and maintenance cost of distributed power generation DG; C 环保Indicates the environmental protection cost savings brought about by installing distributed power generation DG; C 能源 Indicates the primary energy saving cost brought about by installing distributed power generation DG, Indicates the cost of reducing network loss brought about by the overall transformation project, C 弃能惩罚 It refers to the fines that distributed power sources need to pay for curtailing wind and solar power.

[0108] The expression of the operation and maintenance cost of distributed power generation DG is:

[0109] C OP =C0+C m (16)

[0110]

[0111]

[0112] Where: C0 and C m are operating costs and maintenance costs respectively; N is the number of years; a is the operating cost per unit of electricity; Δt is the time period length (1 hour in this invention);

[0113] Among them, P iwd is the active power generated by the distributed power supply of the wind turbine at the i-th moment, P ipv is the active power generated by the photovoltaic cell distributed power supply at the i-th moment; b wd b represents the maintenance cost coefficient of the distributed power supply of wind turbines; pv Represents the maintenance cost coefficient of the distributed power generation of wind turbines.

[0114] The expression for the environmental protection cost savings brought about by installing distributed power generation DG is:

[0115]

[0116] Where: P carbon represents the carbon tax price per kilowatt-hour.

[0117] The primary energy saving cost brought about by installing distributed power generation DG is expressed as:

[0118]

[0119] Where: P coal Indicates the cost per kilowatt-hour of thermal power generation.

[0120] The cost expression for network loss reduction brought about by the overall transformation project is:

[0121] C Δploss =(P lossbf -P lossaf )P elc(twenty one)

[0122] Where: P lossbf It represents the total network loss before transformation, P lossaf It represents the total network loss after transformation, P elc Indicates the unit electricity price.

[0123] Lower level constraints:

[0124] The lower-level model takes into account the specific operation conditions of the network. It not only needs to optimize the optimization variables, but also needs to constrain its network currents.

[0125] St

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] Where: is the maximum available power of the i-th DG in time period t; ω t is the maximum available output rate of DG in period t. are the voltage amplitude and phase angle difference of nodes i and j respectively; G ii 、B ii , G ij 、B ij are the self-conductance, self-susceptance, mutual conductance and mutual susceptance in the node admittance matrix respectively; are the upper and lower limits of the node voltage amplitude, which are 0.95 and 1.05 respectively; is the upper limit of the branch current amplitude.

[0133] Equations (1)-(27) constitute a three-layer planning model for the coordinated optimization configuration of VSC and DG. The interactive relationship diagram of each layer model is as follows: Figure 2 shown.

[0134] The three-layer coordination planning model of VSC and DG mentioned above is solved by a hybrid method of enumeration method, elite-preserving genetic algorithm and cone programming. The enumeration method is used for the site selection and sizing plan of the upper layer VSC, the elite-preserving genetic algorithm is used to obtain the site selection and sizing plan of the DG, and cone programming is used to solve the operation optimization problem of the lower layer.

[0135] S2: Solve the multi-layer model from bottom to top based on a pre-set algorithm to obtain the capacities of the distributed power source and the voltage source converter.

[0136] (1) Upper-level model solution.

[0137] As previously mentioned in the present invention, DC transformation projects cannot be implemented on a large scale and in full scope. Therefore, there are not many feasible solutions for the upper model. Therefore, the enumeration method is used to solve the problem. The enumeration method is based on the following:

[0138] Constraints on the number of nodes in the transformed line:

[0139] 5≤n i ≤10 (28)

[0140] Constraints on the proportion of DC load on the transformed lines:

[0141]

[0142] VSC capacity constraints of the transformed lines:

[0143] S vsc ≥∑(P dc +P ac ) (30)

[0144] Where: n i Indicates the number of nodes in the line, P dc Indicates the annual average power of the DC load in the line, P ac Indicates the annual average power of the AC load in the line, S vsc Indicates the line VSC capacity.

[0145] (2) Middle-level model solution.

[0146] The middle-level model is solved by using the elite-preserving genetic algorithm to encode the installation location and capacity of the distributed power generation at a given candidate location. The elite-preserving genetic algorithm encoding diagram is as follows: Figure 3 As shown in the figure. k represents the capacity of distributed generation installed at candidate location k, when S k When the capacity is 0, it means that no distributed generation will be installed at the candidate location.

[0147] In standard genetic algorithms, genetic drift often occurs due to improper selection of operations such as replication, crossover, and mutation. This can cause the best individuals in the current population to be lost in the next generation, leading to slow algorithm convergence. An elitist retention strategy preserves the best individuals (those with the highest fitness in the population) that have emerged so far in the population evolution and replicates them to the next generation with probability 1. This genetic algorithm, incorporating an elitist retention strategy, significantly improves global convergence compared to standard genetic algorithms.

[0148] (3) Solution of the lower-level model.

[0149] The lower-level model is solved using cone programming. The constraints in the model that do not conform to the second-order cone form are converted using the cone optimization algorithm to achieve rapid and effective solution. It is converted into YALMIP in the MATLAB environment and solved using the CPLEX algorithm package.

[0150] The overall solution flow chart is as follows Figure 4 shown.

[0151] S3: Transforming the AC / DC hybrid distribution network based on the capacities of the distributed power sources and the voltage source converters.

[0152] Example 2:

[0153] This embodiment provides an AC / DC hybrid distribution network reconstruction planning system, the system comprising:

[0154] Modeling module: building a multi-layer model based on the AC / DC hybrid distribution network transformation project type;

[0155] Solving module: solving the multi-layer model from bottom to top based on a pre-set algorithm to obtain the capacity of the distributed power supply and the voltage source converter;

[0156] Transformation module: transforming the AC / DC hybrid distribution network based on the capacity of the distributed power supply and the voltage source converter;

[0157] Among them, the types of transformation projects include: network optimization, distributed power planning and DC transformation planning;

[0158] The multi-layer model includes: a lower layer model constructed based on network optimization, a middle layer model constructed based on distributed power supply planning, and an upper layer model constructed based on DC transformation planning.

[0159] The solution module includes:

[0160] The first solving submodule: solves the lower model by enumeration method to obtain the active power value of the distributed power supply;

[0161] The second solution submodule: Based on the unit capacity and active power value of the distributed power source, the installation quantity of the distributed power source is obtained through the elite retention genetic algorithm;

[0162] The third solving submodule: based on the installation quantity and the unit capacity of the voltage source converter, the capacity of the voltage source converter is solved by a cone programming method.

[0163] The modeling module includes:

[0164] Lower-level objective function construction submodule: constructs the lower-level model objective function based on the active power output of the distributed power supply;

[0165] Lower-level constraint condition construction submodule: set the lower-level model constraint conditions to obtain the lower-level model;

[0166] The lower-level model constraints include: available output constraints of distributed power sources, output constraints of distributed power sources, penetration constraints of single distributed power sources, total penetration constraints of distributed power sources, active and reactive power balance constraints of power flow equations, node voltage constraints, and branch voltage constraints.

[0167] The distributed power supply includes: a generator set and a photovoltaic cell.

[0168] The objective function constructed in the lower layer objective function construction submodule is shown in the following formula:

[0169]

[0170] Where N represents the number of VSCs installed, C vsc represents the unit capacity investment cost of VSC, S vsc represents the installed capacity of VSC, a is the unit power operation cost, b is the maintenance cost coefficient of VSC, Δt is the time period length (1h in this invention); P i is the active power flowing out of VSC at the i-th moment, Z mid represents the optimal solution at the middle level, D represents the number of running days, and h represents the number of running hours.

[0171] The modeling module further includes:

[0172] Middle-level objective function construction submodule: constructs the middle-level model objective function based on the capacity and active power value of the distributed power supply;

[0173] Middle-level constraint condition construction submodule: set the middle-level model constraint conditions and obtain the middle-level model;

[0174] The middle-level model constraints include: a maximum capacity constraint of distributed power sources, a unit capacity constraint of distributed power sources, and a total installed capacity constraint of distributed power sources.

[0175] The middle-level model objective function constructed in the middle-level objective function construction submodule is shown in the following formula:

[0176]

[0177] Where N represents the number of DG installations, C wdDG represents the unit capacity investment cost of wind turbine DG, S wdDG Indicates the installed capacity of wind turbine DG; CpvDG represents the unit capacity investment cost of photovoltaic cells DG, S pvDG represents the installed capacity of photovoltaic cells DG, Z down is the optimal solution for the lower model.

[0178] The modeling module further includes:

[0179] An upper-level objective function construction submodule: constructing an upper-level model objective function based on the unit capacity of the distributed power supply and the voltage source converter;

[0180] Upper-level constraint condition construction submodule: sets the upper-level model constraint conditions to obtain the upper-level model;

[0181] The upper model constraint conditions include: voltage source converter maximum capacity constraint, voltage source converter unit capacity constraint and voltage source converter through power constraint.

[0182] The upper-level model objective function constructed in the upper-level objective function construction submodule is shown in the following formula:

[0183]

[0184] Where N represents the number of DG installations, C wdDG represents the unit capacity investment cost of wind turbine DG, S wdDG represents the installed capacity of wind turbine DG, C pvDG represents the unit capacity investment cost of photovoltaic cells DG, S pvDG represents the installed capacity of photovoltaic cells DG, P iwd is the active power generated by the distributed power supply of the wind turbine at the i-th moment, P ipv is the active power generated by the photovoltaic cell distributed power supply at the i-th moment, D represents the number of operating days, and h represents the operating hours.

[0185] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0186] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0187] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0188] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0189] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0190] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for transforming an AC / DC hybrid distribution network, characterized in that: include: Building a multi-layer model based on the AC / DC hybrid distribution network transformation project type; Solving the multi-layer model from bottom to top based on a pre-set algorithm to obtain the capacities of the distributed power source and the voltage source converter; Transforming the AC / DC hybrid distribution network based on the capacity of the distributed power supply and the voltage source converter; The types of transformation projects include: network optimization, distributed power planning and DC transformation planning; The multi-layer model includes: a lower layer model constructed based on network optimization, a middle layer model constructed based on distributed power generation planning, and an upper layer model constructed based on DC transformation planning; The objective function of the lower model is shown in the following formula: In the above formula, C op represents the operation and maintenance cost of distributed power generation DG, C 环保 represents the environmental protection cost savings brought about by installing distributed power generation DG, C 能源 Indicates the primary energy saving cost brought about by installing distributed power generation DG, Indicates the cost of reducing network loss brought about by the overall transformation project, C 弃能惩能 Indicates the fines that distributed power generation needs to pay for curtailing wind and solar power; Among them, the expression of the operation and maintenance cost of distributed power generation DG is: In the above formula, C0 and C m are the operating cost and maintenance cost of distributed power generation DG, N is the number of years, a is the operating cost per unit of electricity, Δt is the time period length, P iwd is the active power generated by the distributed power supply of the wind turbine at the i-th moment, P ipv is the active power generated by the photovoltaic cell distributed power supply at the i-th moment, C wdDG represents the unit capacity investment cost of wind turbine DG, S wdDG represents the installed capacity of wind turbine DG, C pvDG represents the unit capacity investment cost of photovoltaic cells DG, S pvDG represents the installed capacity of photovoltaic cells DG, b wd represents the maintenance cost coefficient of the distributed power supply of wind turbines, b pv Indicates the maintenance cost coefficient of the distributed power supply of the photovoltaic unit; The objective function of the middle-level model is shown below: minZ mid =C inv +Z down In the above formula, C inv represents the investment cost of distributed power equipment, Z down Represents the objective function of the lower model; Among them, the expression of distributed power equipment investment cost is: In the above formula, N represents the number of DG installations, C wdDG represents the unit capacity investment cost of wind turbine DG, S wdDG represents the installed capacity of wind turbine DG, C pvDG represents the unit capacity investment cost of photovoltaic cells DG, S pvDG represents the installed capacity of photovoltaic cells DG; The objective function of the upper model is shown as follows: In the above formula, C inv represents the VSC equipment investment cost, C OP Indicates the VSC equipment operation and maintenance costs, It represents the gross national product created by the additional load of the power grid after the overall transformation compared with that before the transformation, Z mid Represents the objective function of the middle-level model; Among them, the expression of VSC equipment investment cost is: In the above formula, N represents the number of VSCs installed, C vsc represents the unit capacity investment cost of VSC, S vsc Indicates the installed capacity of the VSC; The expression of VSC equipment operation and maintenance cost is as follows: In the above formula, C0 and C m are the operating cost and maintenance cost of the VSC equipment, N is the number of years, a is the operating cost per unit of electricity, Δt is the time period, and P i is the active power flowing out of the VSC at the i-th moment, and b represents the maintenance cost coefficient of the VSC.

2. The method according to claim 1, wherein The multi-layer model is solved from bottom to top based on a pre-set algorithm to obtain the capacity of the distributed power supply and the voltage source converter, including: The active power value of the distributed generation is obtained by solving the lower model through the cone programming method; Based on the unit capacity and active power value of distributed power sources, the installation quantity of distributed power sources is obtained by solving the elite-retention genetic algorithm; Based on the installation quantity and the unit capacity of the voltage source converter, the capacity of the voltage source converter is obtained by enumeration method.

3. The method according to claim 2, wherein The lower-layer model constructed based on network optimization includes: Based on the active power output of distributed power sources, the objective function of the lower model is constructed; Set the constraints of the lower model to obtain the lower model; The constraints of the lower model include: available output constraints of distributed power sources, output constraints of distributed power sources, penetration constraints of single distributed power sources, total penetration constraints of distributed power sources, active and reactive power balance constraints of power flow equations, node voltage constraints, and branch voltage constraints; The distributed power source includes: a wind turbine and photovoltaic cells.

4. The method according to claim 2, wherein The middle-level model constructed based on distributed power generation planning includes: Based on the capacity and active power value of distributed generation, the objective function of the middle-level model is constructed; Set the constraints of the middle-level model to obtain the middle-level model; The middle-level model constraints include: a maximum capacity constraint of distributed power sources, a unit capacity constraint of distributed power sources, and a total installed capacity constraint of distributed power sources.

5. The method according to claim 2, wherein The upper-level model constructed based on the DC transformation plan includes: constructing an upper model objective function based on the unit capacity of the distributed power source and the voltage source converter; Set the upper model constraint conditions to obtain the upper model; The upper model constraint conditions include: voltage source converter maximum capacity constraint, voltage source converter unit capacity constraint and voltage source converter through power constraint.

6. The system of the AC / DC hybrid distribution network transformation method according to claim 1, characterized in that: The system comprises: Modeling module: building a multi-layer model based on the AC / DC hybrid distribution network transformation project type; Solving module: solving the multi-layer model from bottom to top based on a pre-set algorithm to obtain the capacity of the distributed power supply and the voltage source converter; Transformation module: transforming the AC / DC hybrid distribution network based on the capacity of the distributed power supply and the voltage source converter; Among them, the types of transformation projects include: network optimization, distributed power planning and DC transformation planning; The multi-layer model includes: a lower layer model constructed based on network optimization, a middle layer model constructed based on distributed power supply planning, and an upper layer model constructed based on DC transformation planning.

7. The system according to claim 6, wherein: The solution module includes: The first solving submodule: solves the lower model through cone programming method to obtain the active power value of distributed power supply; The second solution submodule: Based on the unit capacity and active power value of the distributed power source, the installation quantity of the distributed power source is obtained through the elite retention genetic algorithm; The third solution submodule: based on the installation quantity and the unit capacity of the voltage source converter, obtains the capacity of the voltage source converter by enumeration method.

8. The system according to claim 7, wherein: The modeling module includes: Lower-level objective function construction submodule: constructs the lower-level model objective function based on the active power output of the distributed power supply; Lower-level constraint condition construction submodule: set the lower-level model constraint conditions to obtain the lower-level model; The constraints of the lower model include: available output constraints of distributed power sources, output constraints of distributed power sources, penetration constraints of single distributed power sources, total penetration constraints of distributed power sources, active and reactive power balance constraints of power flow equations, node voltage constraints, and branch voltage constraints; The distributed power source includes: a wind turbine and photovoltaic cells.

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