Main distribution micro cooperative electric power and electric quantity balancing method and system considering novel grid-connected main body

Abstract

The invention relates to a main distribution micro cooperative electric power and electric quantity balancing method and system considering a novel grid-connected main body. The method comprises the following steps: S1, presetting a power balancing model; s2, inputting node position data and historical operation data of the target power system; s3, predicting a predicted net load value at the current operation moment t, and implementing clustering partition to obtain a clustering tree; s4, adjusting the layered and partitioned structure of the target power system by adopting a layered clustering method; s5, outputting a power resource allocation result under the three-level cooperation of the main power distribution unit and the micro power distribution unit; and S6, judging whether the current running time t is an end time or not. Through the power and electric quantity balance method based on dynamic layering and partitioning, the partitioning result can be dynamically adjusted according to the real-time operation state of the power system, power and electric quantity balance of main distribution micro three-level cooperation is achieved, the power generation cost can be reduced, the absorption capacity of new energy can be improved, and the power generation efficiency is improved. And the stability and reliability of the power system under the peak load are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and particularly relates to a main-distribution-micro collaborative power and electricity balance method and system considering new grid-connected entities. Background Technique

[0002] With the transformation of the global energy structure and the rapid development of smart grid technology, the power and electricity balance problem in the power system has become increasingly complex. The traditional centralized dispatching method is difficult to adapt to the large-scale access of distributed energy sources (such as photovoltaic and wind power) and the resulting volatility and uncertainty. At the same time, the diverse needs of power users, the significant volatility of electricity loads, and the introduction of various adjustable resources (such as energy storage devices and controllable loads) further exacerbate the difficulty of power system balance.

[0003] The access of distributed energy sources has changed the power supply and demand structure of electricity, making the power system no longer rely on a single centralized power source, but rather needs to consider the dynamic characteristics of a large number of regional power sources and loads. For example, in typical industrial parks, commercial areas, and residential areas, the characteristics of user demands and the distribution of power resources vary greatly, and it is difficult for traditional power systems to achieve effective regional management and efficient energy dispatching. In addition, the emergence of microgrids has further promoted the development of the power system towards regional autonomy. Microgrids have the ability to achieve local power self-balancing and can also interact with the main grid when necessary.

[0004] Therefore, in the case of ensuring power supply during peak periods, considering a main-distribution-micro collaborative power and electricity balance method for new grid-connected entities to solve the difficulties existing in the prior art is an urgent problem for those skilled in the art. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a main-distribution-micro collaborative power and electricity balance method and system considering new grid-connected entities.

[0006] The technical solution adopted by the present invention is as follows:

[0007] In the first aspect, a main-distribution-micro collaborative power and electricity balance method considering new grid-connected entities is provided, including:

[0008] S1. Preset a power balance model, and set the maximization of the main-distribution-micro hierarchical self-balancing of the power balance model as the primary optimization goal and the minimization of the interactive power as the secondary optimization goal;

[0009] S2. Input the position data of each node of the target power system and the historical operation data within the T0 time period before the current operation time t. The historical operation data includes historical load data, historical generation data, and the operation constraint conditions of new grid-connected entities;

[0010] S3. Calculate the net load value of each node in the target power system at each moment within the time period (t−1−T0, t−1); based on historical load data and the operating constraints of new grid-connected entities, predict the predicted net load value at the current operating moment t, and combine the operating conditions of new grid-connected entities in each region to implement clustering and partitioning to obtain a clustering tree. With power supply guarantee as the goal, generate a dynamic scheduling plan for power resources in different power system regions. The power system regions include the main grid region, the distribution network region, and the microgrid region;

[0011] S4. Based on the clustering tree and the predicted net load values of each node, use the hierarchical clustering method to adjust the hierarchical partitioning structure of the target power system to ensure effective coordinated control among different power system regions;

[0012] S5. Based on the hierarchical partitioning result at the current moment, use the power balance model to calculate the power balance result of the target power system and output the power resource allocation result under the three-level coordination of the main grid, distribution network, and microgrid;

[0013] S6. Determine whether the current operating moment t is the end moment T e ; if so, end the operation; if not, update the moment t = t + 1, return to step S2, and perform hierarchical partitioning and power balance calculation for the next moment.

[0014] Furthermore, the expression for maximizing the hierarchical self-balance of the main grid, distribution network, and microgrid as the primary optimization goal is:

[0015] ;

[0016] where k ∈ {M, D, μ} represents the main grid, distribution network, and microgrid respectively; represents the power generation in system k; represents the load power in system k;

[0017] The expression for the hierarchical power balance of the main grid, distribution network, and microgrid is:

[0018] ;

[0019] represents the power loss in system k;

[0020] The expression for the hierarchical power balance of the main grid, distribution network, and microgrid is:

[0021] .

[0022] Furthermore, the expression for minimizing the interactive power as the secondary optimization goal is:

[0023] ;

[0024] Among them, \(E\) represents the set of interactive connections among the main grid, distribution grid, and microgrid; represents the interactive power between system \(i\) and system \(j\), and the interactive power includes active power and reactive power.

[0025] Furthermore, the operating constraint conditions of the new grid-connected entity include:

[0026] Unit power upper and lower limit constraints, unit ramp rate constraints, wind power and photovoltaic operation constraints, and energy storage operation constraints;

[0027] The unit power upper and lower limit constraints are:

[0028] ;

[0029] The unit ramp rate constraints are:

[0030] ;

[0031] Among them, are the powers of unit \(b\) in partition \(a\) at time \(t\) and \(t - 1\) respectively; are the minimum and maximum values of the output of unit \(b\); and are the maximum ramp-up and ramp-down rates of unit \(b\) respectively;

[0032] The wind power and photovoltaic operation constraints are:

[0033] ;

[0034] ;

[0035] Among them, and are the actual output powers of photovoltaic and wind power at time \(t\) respectively; and are the theoretical output powers of photovoltaic and wind power at time \(t\) respectively; and are the sets of wind turbines and photovoltaic units in partition \(a\) respectively;

[0036] The energy storage operation constraints are:

[0037] ;

[0038] ;

[0039] ;

[0040] Among them, is the energy storage output power at time \(t\), positive for discharging and negative for charging; is the maximum output power of energy storage \(b\); , are the power states of energy storage b at time t and t - 1 respectively; represents the energy storage set of partition a.

[0041] Furthermore, the power balance model aims to minimize the total generation cost of the units and the power imbalance of the partitions, and the expression is:

[0042] ;

[0043] wherein, is the partition set obtained by hierarchical partitioning of the target power system; is a specific partition of the partition set; is the set of units in partition a; is a specific unit in the unit set; is the cost function of unit b; is the output power of unit b at time t; is the power imbalance value of partition a at time t; is the power imbalance penalty term of partition a.

[0044] On the second aspect, a main - distribution - micro collaborative power and electricity balance system considering new grid - connected entities is provided, including:

[0045] A power balance model setting module, used to preset the power balance model, set the maximization of the main - distribution - micro hierarchical self - balance of the power balance model as the primary optimization goal, and the minimization of the interactive power as the secondary optimization goal;

[0046] A data input module, used to input the position data of each node of the target power system and the historical operation data within the T0 time period before the current operation time t. The historical operation data includes historical load data, historical generation data, and the operation constraint conditions of new grid - connected entities;

[0047] A calculation module, used to calculate the net load value of each moment of all nodes in the target power system within the time period (t - 1 - T0, t - 1); predict the predicted net load value at the current operation time t according to the historical load data and the operation constraint conditions of new grid - connected entities, and implement clustering partitioning to obtain a clustering tree in combination with the operation conditions of new grid - connected entities in each region. With the goal of ensuring power supply, generate a dynamic scheduling plan for power resources in different power system regions. The power system regions include the main grid region, the distribution grid region, and the micro - grid region;

[0048] A hierarchical partitioning structure adjustment module, used to adjust the hierarchical partitioning structure of the target power system by using the hierarchical clustering method based on the clustering tree and the predicted net load values of each node, so as to ensure effective collaborative control between different power system regions;

[0049] A power balance calculation module, configured to calculate the power balance result of a target power system by using a power balance model based on the hierarchical partition result at the current moment, and output the power resource allocation result under the three-level coordination of the main, distribution, and micro grids;

[0050] A judgment module, configured to judge whether the current operation moment t is the end moment T e ; if so, end the operation; if not, update the moment t = t + 1, return to step S2, and perform hierarchical partitioning and power balance calculation for the next moment.

[0051] The beneficial effects achieved by the present invention:

[0052] Through the power and electricity balance method based on dynamic hierarchical partitioning, the partition result can be dynamically adjusted according to the real-time operation state of the power system, realizing the power and electricity balance of the three-level coordination of the main, distribution, and micro grids. It can not only reduce the generation cost, but also improve the consumption capacity of new energy, and effectively enhance the stability and reliability of the power system under peak load. Description of the Drawings

[0053] Figure 1 It is a flowchart of the main, distribution, and micro grid collaborative power and electricity balance method of the present invention considering new grid-connected entities;

[0054] Figure 2 It is a structure diagram of the main, distribution, and micro grid collaborative power and electricity balance system of the present invention considering new grid-connected entities. Detailed Embodiments

[0055] The present invention will be further described below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0056] Based on the idea of dynamic hierarchical partitioning and three-level coordination of the main, distribution, and micro grids, the present invention proposes a main, distribution, and micro grid collaborative power and electricity balance method considering new grid-connected entities under peak power supply guarantee, which can change the partition result in real time, enhance the reliability of the regional power grid, and promote the power grid to achieve dynamic power and electricity self-balancing.

[0057] As Figure 1 shown, the embodiment of the present invention provides a main, distribution, and micro grid collaborative power and electricity balance method considering new grid-connected entities, including:

[0058] S1. Preset a power balance model, and set the maximization of the hierarchical self-balancing of the main, distribution, and micro grids in the power balance model as the primary optimization goal, and the minimization of the interactive power as the secondary optimization goal;

[0059] In this embodiment, the expression for the maximization of the hierarchical self-balancing of the main, distribution, and micro grids in the preset power balance model as the primary optimization goal is:

[0060] ;

[0061] Among them, k ∈ {M, D, μ} represents the main grid, distribution grid, and microgrid respectively; represents the power generation power within system k; represents the load power within system k; The primary optimization goal of maximizing the hierarchical self - balance of the main, distribution, and microgrids is to minimize the power interaction between cross - level systems and improve the self - balance ability of the system;

[0062] The expression for the hierarchical power balance of the main, distribution, and microgrids is:

[0063] ;

[0064] represents the power loss within system k;

[0065] The expression for the hierarchical power balance of the main, distribution, and microgrids is:

[0066] ;

[0067] The expression for minimizing the interaction power as the secondary optimization goal is:

[0068] ;

[0069] Among them, E represents the set of interactive connections between the main grid, distribution grid, and microgrid; represents the interaction power between system i and system j, and the interaction power includes active and reactive power;

[0070] Taking the maximization of the hierarchical self - balance of the main, distribution, and microgrids as the primary optimization goal and the minimization of the interaction power as the secondary optimization goal, and integrating them into a multi - objective optimization problem, usually in the form of weighted summation:

[0071] ;

[0072] Among them, w1 and w2 are weight coefficients used to adjust the priorities of self - balance and interaction power optimization; J is the value of the comprehensive objective function.

[0073] S2. Input the location data of each node of the target power system and the historical operation data within the T0 time period before the current operation time t. The historical operation data includes historical load data, historical generation data, and the operation constraint conditions of new grid - connected entities;

[0074] In this embodiment, the operation constraint conditions of new grid - connected entities include the upper and lower limits of unit power, unit ramp - up constraint, wind power and photovoltaic operation constraints, and energy storage operation constraints;

[0075] The upper and lower limits of unit power constraint are:

[0076] ;

[0077] The ramp rate constraint of the unit is:

[0078] ;

[0079] Wherein, are the powers of unit b in area a at time t and t - 1 respectively; are the minimum and maximum values of the output of unit b; 、 are the maximum ramp - up and ramp - down rates of unit b respectively;

[0080] The operation constraints of wind power and photovoltaic are:

[0081] ;

[0082] ;

[0083] Wherein, and are the actual output powers of photovoltaic and wind power at time t respectively; and are the theoretical output powers of photovoltaic and wind power at time t respectively; and are the sets of wind turbines and photovoltaic units in area a respectively;

[0084] The operation constraints of energy storage are:

[0085] ;

[0086] ;

[0087] ;

[0088] Wherein, is the energy storage output power at time t, positive for discharging and negative for charging; is the maximum output power of energy storage b; 、 are the state - of - charge of energy storage b at time t and t - 1 respectively; represents the set of energy storage in area a.

[0089] S3. Calculate the net load value of each node in the target power system at each moment within the time period (t - 1 - T0, t - 1); based on the historical load data and the operating constraints of the new grid-connected entities, predict the predicted net load value at the current operating moment t, and combine the operating conditions of the new grid-connected entities in each region to implement clustering and partitioning to obtain a clustering tree, and generate a dynamic scheduling plan for power resources in different power system regions with the goal of ensuring power supply. The power system regions include the main grid region, the distribution network region, and the microgrid region;

[0090] S4. Based on the clustering tree and the predicted net load values of each node, use the hierarchical clustering method to adjust the hierarchical partitioning structure of the target power system to ensure effective coordinated control between different power system regions;

[0091] In this embodiment, based on the clustering tree and the predicted net load values of each node, use the hierarchical clustering method to adjust the hierarchical partitioning structure of the power system to ensure effective coordinated control between different power system regions (main grid, distribution network, microgrid); in particular, consider the interactive power constraint conditions of the tie lines, optimize the three-level coordinated power and electricity balance of the main, distribution, and micro levels, and fully incorporate the operating constraints of the above-described wind power, photovoltaic, and energy storage units;

[0092] Input the two-dimensional geographical location data of each node in the power grid and normalize the data;

[0093] Regard each grid node as a class, and then determine the two classes with the closest average distance to be merged;

[0094] Repeat the merging process until all grid nodes are merged into one class, and output the clustering result after each merge;

[0095] Secondly, establish an objective function with the minimum number of interconnection lines and the maximum sum of squares of the total net load in the partition to determine a reasonable number of partitions:

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] Among them, is the square root of the sum of the squares of the net load values of all nodes in partition k at time t; is the net load value of node j in partition a at time t when the number of partitions is k; is The L2 norm of; is the total number of interconnection lines for each partition at time t when the number of partitions is k; is The L2 norm of; is the number of interconnection lines at time t when the number of partitions is k; λ is the weighting coefficient.

[0102] S5. Based on the hierarchical partitioning result at the current moment, use the power balance model to calculate the power balance result of the target power system, and output the power resource allocation result under the three-level coordination of the main distribution and microgrid;

[0103] In this embodiment, considering the economy of system operation and the ability of independent partition operation, a power balance model is established;

[0104] The power balance model takes the weighted sum of minimizing the total generation cost of the units and the power imbalance of each partition as the objective function, and the expression is:

[0105] ;

[0106] Among them, is the partition set obtained by hierarchical partitioning of the target power system; is a specific partition of the partition set; is the set of units in partition a; is a specific unit in the unit set; is the cost function of unit b; is the output power of unit b at time t; is the power imbalance value of partition a at time t; is the power imbalance penalty term of partition a; The constraint conditions are respectively:

[0107] Power balance constraint:

[0108] ;

[0109] In the formula: , , , are respectively the node admittance matrix, node phase angle matrix, adjacency matrix and interconnection line flow matrix of partition a at time t; , , , are respectively the sum of the output of conventional units, load, actual wind power output and actual photovoltaic power generation in partition a at time t;

[0110] Phase angle constraint of the reference node:

[0111] ;

[0112] is the reference node phase angle of partition a;

[0113] Power flow constraints of lines in the partition:

[0114] ;

[0115] In the formula: is the power flow transfer matrix of partition a; is the upper limit matrix of the power flow of the lines within partition a;

[0116] Power flow constraints of the interconnected lines:

[0117] ;

[0118] In the formula: is the power flow of the interconnected line at time t in partition a; is the upper limit of the power flow on the interconnected line within partition a; is the set of the interconnected lines within partition a; are the phase angles of nodes i and j at time t respectively; is the reactance value of line ij;

[0119] Regional power imbalance constraint:

[0120] ;

[0121] In the formula: is the power imbalance value of partition a at time t.

[0122] S6. Determine whether the current operation time t is the end time T e ; if so, end the operation; if not, update the time t = t + 1, return to step S2, and perform the hierarchical partitioning and power balance calculation for the next time.

[0123] Beneficial effects achieved by the present invention:

[0124] S1. Preset a power balance model, and set the maximization of the primary and secondary distribution micro-level self-balancing of the power balance model as the primary optimization goal, and the minimization of the interactive power as the secondary optimization goal; S2. Input the position data of each node of the target power system and the historical operation data within the T0 time period before the current operation time t. The historical operation data includes historical load data, historical power generation data, and the operation constraint conditions of the new grid-connected entities; S3. Calculate the net load value of each node in the target power system at each moment within the time period (t - 1 - T0, t - 1); predict the predicted net load value at the current operation time t based on the historical load data and the operation constraint conditions of the new grid-connected entities, and combine the operation conditions of the new grid-connected entities in each region to implement clustering and partitioning to obtain a clustering tree, and generate a dynamic scheduling plan for the power resources in different power system regions with power supply guarantee as the goal. The power system regions include the main grid region, the distribution network region, and the microgrid region; S4. Based on the clustering tree and the predicted net load value of each node, use the hierarchical clustering method to adjust the hierarchical partitioning structure of the target power system to ensure effective coordinated control between different power system regions; S5. Based on the hierarchical partitioning result at the current moment, use the power balance model to calculate the power balance result of the target power system, and output the power resource allocation result under the three-level coordination of the primary, secondary, and micro levels; S6. Determine whether the current operation time t is the end time T e ; if so, end the operation; if not, update the time t = t + 1, return to step S2, and perform the hierarchical partitioning and power balance calculation for the next moment. Through the power and electricity balance method based on dynamic hierarchical partitioning, the partitioning result can be dynamically adjusted according to the real-time operation state of the power system, realizing the power and electricity balance of the three-level coordination of the primary, secondary, and micro levels, which can not only reduce the power generation cost, but also improve the consumption capacity of new energy, and effectively enhance the stability and reliability of the power system under peak load.

[0125] Combined with the primary, secondary, and micro coordinated power and electricity balance method considering new grid-connected entities described in the above embodiments, the primary, secondary, and micro coordinated power and electricity balance system considering new grid-connected entities will be described below through embodiments.

[0126] As Figure 2 shown, an embodiment of the present invention provides a primary, secondary, and micro coordinated power and electricity balance system considering new grid-connected entities, including:

[0127] A power balance model setting module 201, configured to preset a power balance model, and set the maximization of the primary and secondary distribution micro-level self-balancing of the power balance model as the primary optimization goal, and the minimization of the interactive power as the secondary optimization goal;

[0128] A data input module 202, configured to input the position data of each node of the target power system and the historical operation data within the T0 time period before the current operation time t. The historical operation data includes historical load data, historical power generation data, and the operation constraint conditions of the new grid-connected entities;

[0129] A calculation module 203 is configured to calculate the net load value of each node in the target power system at each moment within the time period (t−1−T0, t−1); predict the predicted net load value at the current operating moment t according to historical load data and the operating constraints of new grid-connected entities, and implement clustering and partitioning based on the operating conditions of new grid-connected entities in each region to obtain a clustering tree. With power supply guarantee as the goal, a dynamic scheduling plan for power resources in different power system regions is generated. The power system regions include a main grid region, a distribution network region, and a microgrid region;

[0130] A hierarchical partitioning structure adjustment module 204 is configured to adjust the hierarchical partitioning structure of the target power system by using a hierarchical clustering method based on the clustering tree and the predicted net load value of each node, so as to ensure effective coordinated control between different power system regions;

[0131] A power balance calculation module 205 is configured to calculate the power balance result of the target power system by using a power balance model based on the hierarchical partitioning result at the current moment, and output the power resource allocation result under the three-level coordination of the main grid, distribution network, and microgrid;

[0132] A judgment module 206 is configured to judge whether the current operating moment t is the end moment T e ; if so, end the operation; if not, update the moment t=t+1, return to step S2, and perform hierarchical partitioning and power balance calculation for the next moment.

[0133] The beneficial effects achieved by the present invention:

[0134] Through the power and electricity balance method based on dynamic hierarchical partitioning, the partitioning result can be dynamically adjusted according to the real-time operating state of the power system, realizing the power and electricity balance of the three-level coordination of the main grid, distribution network, and microgrid. It can not only reduce the generation cost, but also improve the consumption capacity of new energy, and effectively improve the stability and reliability of the power system under peak load.

[0135] Preferably, in combination with Figure 2 In some embodiments of the present invention shown in the embodiments, the expression for maximizing the hierarchical self-balance of the main grid, distribution network, and microgrid as the primary optimization goal is:

[0136] ;

[0137] where k∈{M,D,μ} respectively represent the main grid, distribution network, and microgrid; represents the power generation power within system k; represents the load power within system k;

[0138] The expression for the hierarchical power balance of the main grid, distribution network, and microgrid is:

[0139] ;

[0140] Represents the power loss within system k;

[0141] The expression for the primary distribution micro - level power balance is:

[0142] .

[0143] Preferably, in combination with Figure 2 the embodiments shown, in some embodiments of the present invention, the expression for minimizing the interaction power as a secondary optimization objective is:

[0144] ;

[0145] where E represents the set of interaction connections between the main grid, distribution network, and micro - grid; represents the interaction power between system i and system j, and the interaction power includes active and reactive power.

[0146] Preferably, in combination with Figure 2 the embodiments shown, in some embodiments of the present invention, the operating constraint conditions of the new grid - connected entity include:

[0147] Generator power upper and lower limit constraints, generator ramp - rate constraints, wind power and photovoltaic operation constraints, and energy storage operation constraints;

[0148] The generator power upper and lower limit constraints are:

[0149] ;

[0150] The generator ramp - rate constraints are:

[0151] ;

[0152] where are respectively the powers of generator b in partition a at time t and t - 1; are the minimum and maximum values of the output of generator b; , are respectively the maximum ramp - up and ramp - down rates of generator b;

[0153] The wind power and photovoltaic operation constraints are:

[0154] ;

[0155] ;

[0156] where and are respectively the actual output powers of photovoltaic and wind power at time t; and are the theoretical output powers of photovoltaic and wind power at time t respectively; and are the sets of wind turbine units and photovoltaic units in area a respectively;

[0157] The energy storage operation constraints are:

[0158] ;

[0159] ;

[0160] ;

[0161] Among them, is the energy storage output power at time t, positive for discharging and negative for charging; is the maximum output power of energy storage b; and are the state of charge of energy storage b at time t and time t - 1 respectively; represents the set of energy storage in area a.

[0162] Preferably, in combination with Figure 2 shown in the embodiments, in some embodiments of the present invention, the power balance model aims to minimize the total generation cost of the units and the power imbalance of the partitions, and the expression is:

[0163] ;

[0164] Among them, is the set of partitions obtained by hierarchical partitioning of the target power system; is a specific partition of the set of partitions; is the set of units in area a; is a specific unit in the unit set; is the cost function of unit b; is the output power of unit b at time t; is the power imbalance value of area a at time t; is the power imbalance penalty term of area a.

[0165] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0167] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0169] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A main and distribution micro-synergistic power and electricity balance method considering new grid-connected entities, characterized in that Including: S1. Preset a power balance model, and set the maximization of the main - distribution - micro hierarchical self - balance of the power balance model as the primary optimization goal and the minimization of the interactive power as the secondary optimization goal; S2. Input the position data of each node of the target power system and the historical operation data within the time period T0 before the current operation time t. The historical operation data includes historical load data, historical generation data, and the operation constraint conditions of new grid - connected entities; S3. Calculate the net load value of each node in the target power system at each moment within the time period (t - 1 - T0, t - 1); According to the historical load data and the operation constraint conditions of the new grid - connected entities, predict the predicted net load value at the current operation time t, and combine the operation conditions of the new grid - connected entities in each region to implement clustering and zoning to obtain a clustering tree. With the goal of ensuring power supply, generate a dynamic scheduling plan for power resources in different power system regions. The power system regions include the main grid region, the distribution network region, and the micro - grid region; S4. Based on the clustering tree and the predicted net load values of each node, use the hierarchical clustering method to adjust the hierarchical partition structure of the target power system to ensure effective coordinated control among different power system regions; S5. Based on the hierarchical partition result at the current moment, use the power balance model to calculate the power balance result of the target power system and output the power resource allocation result under the three - level coordination of the main - distribution - micro; S6. Determine whether the current running time t is the end time T e ; if so, end the operation; if not, update the time t = t + 1, return to step S2, and perform the hierarchical partitioning and power balance calculation for the next time.

2. The main and distribution micro collaborative power and electricity quantity balance method considering the new grid-connected entity according to claim 1, characterized in that The expression for the maximization of the main - distribution - micro hierarchical self - balance as the primary optimization goal is: ； wherein, k ∈ {M, D, μ} respectively represent the main grid, the distribution grid, and the microgrid; the represents the power generation power within the system k; the represents the load power within the system k; The expression for the main - distribution - micro hierarchical power balance is: ； Said represents the power loss within said system k; The expression for the main - distribution - micro hierarchical power balance is: 。 3. The main and distribution micro-synergy power and electricity balance method considering a new grid-connected entity according to claim 1, characterized in that, The expression for the minimization of the interactive power as the secondary optimization goal is: ； Among them, the E represents the set of interactive connections among the main grid, distribution network, and microgrid; the represents the interactive power between system i and system j, and the interactive power includes active power and reactive power.

4. The main and distribution micro-synergy power and electricity balance method considering the new grid-connected entity according to claim 1, characterized in that, The operation constraint conditions of the new grid - connected entities include: Upper and lower limits of unit power constraint, unit ramping constraint, wind power and photovoltaic operation constraints, and energy storage operation constraints; The upper and lower limits of unit power constraint is: ； The unit ramping constraint is: ； Among them, the are respectively the power of unit b in area a at time t and t-1; the are the minimum and maximum values output by unit b; the , are respectively the maximum ramp-up and ramp-down rates of unit b; The wind power and photovoltaic operation constraints are: ； ； Among them, the and the are respectively the actual output powers of photovoltaic and wind power at time t; the and the are respectively the theoretical output powers of photovoltaic and wind power at time t; the and the are respectively the sets of wind turbine units and photovoltaic units in area a; The energy storage operation constraints are: ； ； ； Among them, the is the energy storage output power at time t, with positive for discharging and negative for charging; the is the maximum output power of energy storage b; the and are the state of charge of energy storage b at time t and time t-1 respectively; represents the energy storage set of partition a.

5. The main and distribution micro-synergistic power and electricity quantity balance method considering a new grid-connected entity according to claim 1, characterized in that The power balance model aims to minimize the total generation cost of units and the power imbalance of partitions, and the expression is: ； Among them, the is the set of partitions obtained by hierarchical partitioning of the target power system; the is a specific partition of the set of partitions; the is the set of units in partition a; the is a specific unit in the set of units; the is the cost function of unit b; the is the output power of unit b at time t; the is the power imbalance value of partition a at time t; the is the power imbalance penalty term of partition a.

6. A main and distribution micro collaborative power and electricity balance system considering new grid-connected entities, characterized in that Including: A power balance model setting module, used to preset a power balance model, and set the maximization of the main - distribution - micro hierarchical self - balance of the power balance model as the primary optimization goal and the minimization of the interactive power as the secondary optimization goal; A data input module, used to input the position data of each node of the target power system and the historical operation data within the time period T0 before the current operation time t. The historical operation data includes historical load data, historical generation data, and the operation constraint conditions of new grid - connected entities; A calculation module, used to calculate the net load value of each node in the target power system at each moment within the time period (t - 1 - T0, t - 1); Predict the predicted net load value at the current operating moment t according to the historical load data and the operating constraint conditions of the new grid-connected entity, and combine the operating conditions of the new grid-connected entity in each region to implement clustering and partitioning to obtain a clustering tree. With power supply guarantee as the goal, generate a dynamic scheduling plan for power resources in different power system regions, where the power system regions include the main grid region, the distribution network region, and the microgrid region; A hierarchical partitioning structure adjustment module, configured to adjust the hierarchical partitioning structure of the target power system by using the hierarchical clustering method based on the clustering tree and the predicted net load value of each node, so as to ensure effective coordinated control among different power system regions; A power balance calculation module, configured to calculate the power balance result of the target power system by using the power balance model based on the hierarchical partitioning result at the current moment, and output the power resource allocation result under the three-level coordination of the main, distribution, and micro grids; A judgment module, which is used to judge whether the current running time t is the end time T e ; if so, end the operation; if not, update the time t = t + 1, return to step S2, and perform the hierarchical partitioning and power balance calculation for the next time.

7. The main and distribution micro-synergistic power and electricity balance system considering a new grid-connected entity according to claim 6, characterized in that, The expression with the maximization of the hierarchical self-balancing of the main, distribution, and micro grids as the primary optimization goal is: ； wherein, k ∈ {M, D, μ} respectively represent the main grid, the distribution grid, and the microgrid; the represents the power generation power within the system k; the represents the load power within the system k; The expression of the hierarchical power balance of the main, distribution, and micro grids is: ； The represents the power loss within the system k; The expression of the hierarchical power balance of the main, distribution, and micro grids is: 。 8. The main and distribution micro-synergistic power and electricity balance system considering the new grid-connected entity according to claim 6, characterized in that, The expression with the minimization of the interactive power as the secondary optimization goal is: ； Among them, the E represents the interactive connection set among the main grid, distribution network, and microgrid; the represents the interactive power between system i and system j, and the interactive power includes active power and reactive power.

9. The main and distribution micro-synergistic power and electricity balance system considering the new grid-connected entity according to claim 6, characterized in that The operating constraint conditions of the new grid-connected entity include: Unit power upper and lower limit constraints, unit ramp rate constraints, wind power and photovoltaic operation constraints, and energy storage operation constraints; The unit power upper and lower limit constraint is: ； The unit ramp rate constraint is: ； Among them, the are the powers of the unit b in partition a at time t and t - 1 respectively; the are the minimum and maximum values output by the unit b; the and are the maximum climbing up and down rates of the unit b respectively; The wind power and photovoltaic operation constraint is: ； ； Among them, the and the are respectively the actual output powers of photovoltaic and wind power at time t; the and the are respectively the theoretical output powers of photovoltaic and wind power at time t; the and the are respectively the sets of wind turbine units and photovoltaic units in area a; The energy storage operation constraint is: ； ； ； Among them, the is the energy storage output power at time t, positive for discharging and negative for charging; the is the maximum output power of energy storage b; the and are the state of charge of energy storage b at time t and time t-1 respectively; represents the energy storage set of partition a.

10. The main and distribution micro-synergistic power and electricity balance system considering a new grid-connected entity according to claim 6, characterized in that The power balance model aims to minimize the total generation cost of the units and the power imbalance in the partition, and the expression is: ； Among them, the is the set of partitions obtained by hierarchical partitioning of the target power system; the is a specific partition of the set of partitions; the is the set of units in partition a; the is a specific unit in the set of units; the is the cost function of unit b; the is the output power of unit b at time t; the is the power imbalance value of partition a at time t; the is the power imbalance penalty term of partition a.

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