Local area autonomous power grid double-layer planning method based on net load analysis

Abstract

The invention relates to a local area autonomous power grid double-layer planning method based on net load analysis. Comprising the following steps: step 1, constructing a cluster collaborative substation contact planning model considering complementation and mutual assistance of inter-station net loads: constructing a multi-objective-based cluster collaborative optimization planning model by taking power supply capacity, the number of main transformer contact channels, the total length of regional contact channels and the peak-valley difference of the net loads of power supply clusters as objective functions; 2, establishing a unit autonomy layer planning model considering source load space-time complementation: constructing a unit autonomy power distribution network optimization planning model by taking the size of power supply capacity, the total length of a newly-built line, the total number of added switches and supply and demand self-balance as objective functions, wherein the power distribution network optimization planning model takes the size of the power supply capacity, the total length of the newly-built line, the total number of the added switches and supply and demand self-balance as objective functions; the method has the advantages that the bilevel planning model is provided, the net load complementation and mutual aid problem is solved, the step-by-step return power flow is reduced, and local consumption and interaction energy minimization are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of distribution networks, and in particular relates to a double-layer planning method for a local autonomous power grid based on net load analysis. Background Art

[0002] The distribution network is an important infrastructure of the modern economy and society and an important part of the new power system. The new distribution system is quite different from the traditional distribution system in terms of "source, network and load". These changes have also brought many new challenges to the optimization planning of the new distribution system. On the source side, the penetration rate of distributed new energy continues to increase, and the local absorption problem caused by its randomness and anti-peak characteristics has gradually become one of the difficulties in the planning of the distribution system. On the grid side, the configuration of distributed energy storage devices and power electronic devices makes the control strategy of the distribution network more and more complex. The joint planning problem of different types of equipment has brought new challenges to the solution of the optimization planning problem of the nested operation strategy. On the load side, the rapid development of energy substitution technologies such as energy stations and electric heating and cooling air conditioners has led to a significant increase in new incremental loads. At the same time, new DC loads such as communication base stations and electric vehicle charging piles account for a large proportion of terminal loads. cases are gradually increasing; most of these loads can deeply participate in the source-load interaction and put forward higher requirements for power supply reliability, thus bringing more uncertainties to the distribution system planning; at the same time, due to the configuration of flexible interconnection devices, energy storage devices and large-scale access of distributed power sources, the flow of the new distribution system has changed from the traditional one-way flow to multi-directional flow, showing new characteristics; therefore, the above problems require the new distribution system to consider a large number of elements with complex new characteristics at the same time in the planning link, making the constraints and planning objectives of the planning problem more complex and diverse, and the traditional planning method is difficult to continue to support the construction of the distribution system; therefore, it is very necessary to provide a two-layer planning method for a local autonomous power grid based on net load analysis, which proposes a two-layer planning model, solves the problem of net load complementarity and mutual assistance, reduces the step-by-step return flow, realizes local consumption and minimizes the interactive energy. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a two-layer planning method for a local autonomous power grid based on net load analysis, which proposes a two-layer planning model, solves the problem of net load complementarity and mutual assistance, reduces the step-by-step return flow, realizes local consumption and minimizes interactive energy.

[0004] The object of the present invention is achieved by: a two-layer planning method for a local autonomous power grid based on net load analysis, the method comprising the following steps:

[0005] Step 1: Construct a “cluster coordination” substation communication planning model that considers the net load complementarity between stations;

[0006] Step 2: Establish a “unit autonomy” layer planning model that takes into account the temporal and spatial complementarity of sources and loads.

[0007] The step 1 specifically comprises the following steps:

[0008] Step 1.1: Construction of objective function;

[0009] Step 1.2: Generation of variables in the main transformer connection relationship matrix;

[0010] Step 1.3: Establishment of global and local inter-station interconnection equilibrium constraints of the main transformer:

[0011] Step 1.4: Establishment of constraint conditions for the total number of communication channels between main transformer stations;

[0012] Step 1.5: Establishment of net load constraint conditions within the power supply cluster: The net load constraint conditions within the power supply cluster should be as follows: P ≥ 0 (8), where: P is the net load of the cluster;

[0013] Step 1.6: Solve the algorithm.

[0014] The construction of the objective function in step 1.1 is specifically as follows: in combination with actual needs, the four objective functions that meet the requirements of maximum power supply capacity, minimum number of communication channels, minimum total length of communication channels, and minimum peak-to-valley difference of net load of power supply cluster are: max psc[L](1), min(P max -P min )(4), where L is the symmetric matrix of the main transformer connection relationship, L ij Indicates the connection relationship between main transformers No. i and No. j; N Σ is the total number of regional main transformers; d ij P is the length of the communication channel between main transformer stations; max is the maximum net load; P min is the minimum net load value.

[0015] The generation of variables in the main transformer contact relationship matrix in step 1.2 is specifically as follows: the generation rule of variables in the main transformer contact relationship matrix is ​​as follows: Where, d lim It is the upper limit of the length of the communication channel allowed to be established between the main transformers of the substation; const is a constant 0 or 1.

[0016] The establishment of the global and local inter-station communication equilibrium constraint conditions of the main transformer in step 1.3 is specifically as follows: from the perspective of the regularity and equilibrium of the inter-station communication of the main transformer of the substation, the established inter-station communication equilibrium constraint conditions of the main transformer are shown in the following formula: Where N ΣN is the total number of main transformers in all substations in the power supply area; (b)Σ 、N (b-1)Σ are the total number of main transformers of the first b and first b-1 substations in the power supply area; L if , L jf They represent the communication relationship between main transformers No. i and No. j and No. f respectively; δ1 is the balance constraint of the number of global communication channels between main transformers at the same station in the power supply area; δ2 is the balance constraint of the number of inter-station communication channels between the main transformer at the same station in a certain substation and any other substation; It is the set of main transformers within the ath substation in the power supply block.

[0017] The establishment of the constraint condition of the total number of communication channels between main transformer stations in step 1.4 is specifically as follows: the constraint condition of the total number of communication channels between main transformer stations of any substation in the power supply area is as follows: Where N Σ is the total number of regional main transformers; L ij Indicates the connection relationship between main transformers No. i and No. j; D s The upper limit of the total number of connections between main transformer stations; It is the set of main transformers within the ath substation in the power supply block.

[0018] The solution algorithm in step 1.6 specifically includes the following steps:

[0019] Step 1.61: Calculation scale analysis of the optimization model of the main transformer station connection structure: The main transformer connection relationship matrix L can be expressed as a block matrix with the connection between each main transformer in the substation as the unit. Since L is a symmetric matrix, except for the case of the in-station connection structure, only the variables in the upper triangle or lower triangle of the matrix need to be analyzed. The connection variables L to be selected between the main transformer stations are: ij The number of is: In the formula, λ(L ij ) represents the number of interconnection variables to be selected between main transformer stations; n represents the number of transformer substations in the study area; N p 、N q Respectively represent the number of main transformers in the p-th and q-th substations;

[0020] Step 1.62: Solution of the multi-objective optimization model of the main transformer interconnection structure based on genetic algorithm: Genetic algorithm is used to solve the model, and the theoretical maximum power supply capacity value of the power supply area under different interconnection structures is used as the fitness standard of genetic evolution. A sorting solution strategy is adopted for the multi-objective optimization problem, namely: meet certain power supply capacity requirements → the number of interconnection channels required in the region is the least → the total length of the regional interconnection channels is the shortest.

[0021] The step 2 specifically includes the following steps:

[0022] Step 2.1: Model construction;

[0023] Step 2.2: Model solution.

[0024] The model construction in step 2.1 is specifically as follows: a unit-autonomous distribution network optimization planning model is constructed with the power supply capacity, the total length of the new line, the total number of switches installed and the self-balancing of supply and demand as the objective function. The specific objective function is shown in the following formula: min|P load -P DG |(12), where P i is the power supply capacity; w is the number of new lines; C i is the length of the i-th newly built line; β is the constant factor for converting switches into lines; Y is the total number of switches installed; P load is the load in the unit; P DG Provides power for the distributed power supply within the unit.

[0025] The model solution in step 2.2 is specifically: using a non-dominated sorting genetic algorithm, which is an effective method for searching the Pareto frontier, to solve the multi-objective optimization model of the "unit autonomy" feeder network planning considering the spatiotemporal complementarity of source and load. The fitness function in the algorithm is composed of an objective function and a constraint violation penalty function, which is composed as follows: In the formula, F1(x i )、F2(x i ) are the fitness functions of maximum power supply capacity and economic investment respectively; f1(x i )、f2(x i ) are the maximum power supply capacity and economic investment respectively; v(x i ) is the total violation of the constraint condition; α1 and α2 are the penalty factors of the two objective functions respectively; if two individuals x a and x b satisfy: Then x a Dominate x b , otherwise they do not dominate each other; all individuals in the population that are not dominated by any other individuals constitute the Pareto solution set; the final decision plan is selected from this solution set.

[0026] Beneficial effects of the present invention: The present invention is a two-layer planning method for a local autonomous power grid based on net load analysis. In use, the present invention proposes a "cluster collaboration-unit autonomy" two-layer planning model for a local autonomous power grid based on net load analysis. The "cluster collaboration layer" planning focuses on solving the problem of net load complementarity and mutual assistance between stations, and reducing the step-by-step return flow in the high-penetration distributed power source scenario; the "unit autonomy layer" focuses on solving the feeder networking problem considering the temporal and spatial complementarity of sources and loads, realizing local consumption of distributed power sources and minimizing the interaction energy between units, and finally obtaining a distribution network planning and construction plan that achieves Pareto optimality in terms of economy and safety; the present invention has the advantages of proposing a two-layer planning model, solving the problem of net load complementarity and mutual assistance, reducing the step-by-step return flow, and realizing local consumption and minimization of interaction energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart for solving the multi-objective optimization model of the main transformer station connection structure based on GA of the present invention.

[0028] Figure 2 It is a schematic diagram of the IEEE-33 node calculation example system of the present invention.

[0029] Figure 3 It is a schematic diagram of the grid structure and line selection scheme in Example 1 of the present invention.

[0030] Figure 4 It is a schematic diagram of the grid structure and line selection scheme in Example 2 of the present invention. DETAILED DESCRIPTION

[0031] The typical process of distribution system planning is "load forecasting, power and electricity balance, power station site selection, and line planning". In the construction of the new distribution system, on the basis of the grid planning concept, full consideration should be given to the integration of source, grid, load and storage within the smallest unit of the new power system and the mutual support of the surrounding clusters. Through the overall optimization of the uncertainty resources, controllable resources and load demand within the new power system, the local consumption of new energy and the balance of energy supply and demand, regional self-management, and wide-area collaborative control between regions can be achieved. In order to meet these challenges, the cluster collaboration layer and the unit autonomy layer are taken into account at the same time to achieve the optimal planning of the local autonomous power grid. The present invention proposes a "cluster collaboration-unit autonomy" two-layer planning model for the local autonomous power grid based on net load analysis. The "cluster collaboration layer" planning focuses on solving the problem of net load complementarity and mutual assistance between stations, and reducing the step-by-step return flow in the high-penetration distributed power source scenario; the "unit autonomy layer" focuses on solving the feeder networking problem considering the temporal and spatial complementarity of sources and loads, realizing the local consumption of distributed power sources and minimizing the interactive energy between units, and finally obtaining a distribution network planning and construction plan that achieves Pareto optimality in terms of economy and safety.

[0032] The present invention will be further described below in conjunction with the accompanying drawings.

[0033] Example 1

[0034] like Figure 1-4 As shown, a two-layer planning method for a local autonomous power grid based on net load analysis comprises the following steps:

[0035] Step 1: Construct a “cluster coordination” substation communication planning model that considers the net load complementarity between stations;

[0036] Step 2: Establish a “unit autonomy” layer planning model that takes into account the temporal and spatial complementarity of sources and loads.

[0037] The present invention is a two-layer planning method for a local autonomous power grid based on net load analysis. In use, the present invention proposes a "cluster collaboration-unit autonomy" two-layer planning model for a local autonomous power grid based on net load analysis. The "cluster collaboration layer" planning focuses on solving the problem of net load complementarity and mutual assistance between stations, and reducing the step-by-step return flow in the high-penetration distributed power source scenario; the "unit autonomy layer" focuses on solving the feeder networking problem considering the temporal and spatial complementarity of sources and loads, realizing the local consumption of distributed power sources and minimizing the interactive energy between units, and finally obtaining a distribution network planning and construction plan that achieves Pareto optimality in terms of economy and safety; the present invention has the advantages of proposing a two-layer planning model, solving the problem of net load complementarity and mutual assistance, reducing the step-by-step return flow, and realizing local consumption and minimization of interactive energy.

[0038] Example 2

[0039] like Figure 1-4 As shown, a two-layer planning method for a local autonomous power grid based on net load analysis comprises the following steps:

[0040] Step 1: Construct a “cluster coordination” substation communication planning model that considers the net load complementarity between stations;

[0041] In this embodiment, the establishment of communication channels between main transformer substations helps to increase load transfer paths, improve operational flexibility and regional overall power supply capacity, but sometimes the construction of too many communication channels may not play a significant role in improving power supply capacity, but instead increase the complexity of dispatching and operation, and waste channel corridor resources; therefore, it is necessary to optimize the communication structure between main transformer substations in the power supply area, set up communication channels between main transformer substations in the most reasonable and economical way, maximize the potential power supply capacity of the regional power grid, reduce the peak-to-valley difference of the net load of the power supply cluster, and meet the needs of refined power grid planning; the present invention uses power supply capacity, the number of main transformer communication channels, the total length of regional communication channels and the peak-to-valley difference of the net load of the power supply cluster as objective functions to construct a cluster collaborative optimization planning model based on multiple objectives, specifically:

[0042] ① Construction of objective function: Based on actual needs, the four objective functions that meet the requirements of maximum power supply capacity, minimum number of communication channels, minimum total length of communication channels, and minimum peak-to-valley difference of net load of power supply cluster are: max psc[L](1), min(P max -P min )(4), where L is the symmetric matrix of the main transformer connection relationship, L ij Indicates the connection relationship between main transformers No. i and No. j; N Σ is the total number of regional main transformers; d ij The length of the communication channel between the main transformer stations (which can be calculated from the geographical coordinates of the transformer stations); P max is the maximum net load; P min is the minimum value of net load;

[0043] ② Generation of variables in the main transformer contact relationship matrix: The generation rule of variables in the main transformer contact relationship matrix is ​​as follows: Where, d ij The length of the communication channel between the main transformer stations (which can be calculated from the geographical coordinates of the transformer stations); d lim It is the upper limit of the length of the communication channel allowed between the main transformers of the substation (it is not recommended to establish communication between substations that are too far away); const is a constant 0 or 1. If the main transformers at the same station are connected through the bus tie switch, then const = 1; if there is no communication between the main transformers at the same station, then const = 0;

[0044] ③ Establishment of global and local inter-station communication equilibrium constraint conditions of the main transformer: From the perspective of the regularity and balance of the inter-station communication of the main transformer of the substation, the established inter-station communication equilibrium constraint conditions of the main transformer are shown as follows: Where N Σ N is the total number of main transformers in all substations in the power supply area; (b)Σ 、N (b-1)Σ are the total number of main transformers of the first b and first b-1 substations in the power supply area; L if , L jf They represent the communication relationship between main transformers No. i and No. j and No. f respectively; δ1 is the balance constraint of the number of global communication channels between main transformers at the same station in the power supply area; δ2 is the balance constraint of the number of inter-station communication channels between the main transformer at the same station in a certain substation and any other substation; It is the main transformer set of the a-th substation in the power supply block;

[0045] ④ Establishment of the constraint condition for the total number of communication channels between main transformer stations: The constraint condition for the total number of communication channels between main transformer stations of any substation in the power supply area is as follows: Where N Σ is the total number of regional main transformers; Lij Indicates the connection relationship between main transformers No. i and No. j; D s The upper limit of the total number of contacts between main transformer stations. Generally, the total number of contacts is controlled at 5 or less. It is the main transformer set of the a-th substation in the power supply block;

[0046] ⑤ Establishment of net load constraint conditions within the power supply cluster: The net load constraint conditions within the power supply cluster should be as follows: P ≥ 0 (8), where: P is the net load of the cluster;

[0047] ⑥Solution algorithm: specifically includes the following steps:

[0048] Step S1: Calculation scale analysis of the optimization model of the main transformer station communication structure: The optimization problem of the main transformer station communication structure is essentially the process of finding the main transformer communication relationship matrix that meets the given target conditions. The main transformer communication relationship matrix L can be expressed as a block matrix based on the communication between the main transformers in the substation. Since L is a symmetric matrix, except for the case of the in-station communication structure (known), it is only necessary to analyze the variables in the upper triangle or lower triangle of the matrix. The communication variable L to be selected between the main transformer stations ij The number of is: In the formula, λ(L ij ) represents the number of interconnection variables to be selected between main transformer stations; n represents the number of transformer substations in the study area; N p 、N q Represent the number of main transformers in the pth and qth substations respectively; if the exhaustive method or binary implicit enumeration method is used to find the optimization variable, that is, the connection relationship matrix L, the overall calculation scale required is As the number of substations and main transformers in the associated radius increases, the calculation scale will rise rapidly. Therefore, artificial intelligence algorithms are needed to adapt to the optimization calculation of the communication structure between main transformer stations in large-scale networks.

[0049] Step S2: Introduction to the basic concepts of genetic algorithms and their applications in distribution networks: Genetic algorithms (GAs) introduce the mechanisms of natural inheritance and natural selection in nature into mathematical theory. They are a highly parallel, random and adaptive optimization algorithm based on "survival of the fittest". They represent the solution of a problem as a "chromosome" survival process, through the continuous evolution of the "chromosome" group from generation to generation, including operations such as replication, crossover and mutation, and finally converge to the individual that is "most adapted to the environment", thereby obtaining the optimal solution or satisfactory solution to the problem. With the development of computer technology, GAs have received more and more attention and have been widely used in the field of power system optimization planning, such as distribution network reconstruction, distribution network optimization planning, multi-objective multi-regional planning, etc.

[0050] Step S3: Solving the multi-objective optimization model of the main transformer interconnection structure based on genetic algorithm: For the large-scale combinatorial optimization problem of main transformer interconnection structure optimization, a genetic algorithm is used to solve the model. The theoretical maximum power supply capacity value of the power supply area under different interconnection structures is used as the fitness standard of genetic evolution. A sorting solution strategy is adopted for the multi-objective optimization problem, namely: meet certain power supply capacity requirements → the number of interconnection channels required in the region is the least → the total length of the regional interconnection channels is the shortest.

[0051] It should be pointed out that in order to avoid blindly pursuing the theoretical maximum power supply capacity and failing to find the optimal solution that meets the balance requirements of the connection structure, a threshold parameter β (β <= 1) of the power supply capacity is introduced in the screening process of the connection relationship matrix set Ω1(L), and finally a suitable connection relationship matrix is ​​screened out as the Pareto optimal solution of the model.

[0052] Step 2: Establish a “unit autonomy” layer planning model that takes into account the temporal and spatial complementarity of sources and loads.

[0053] In this embodiment, step 2 includes the following steps:

[0054] Step 2.1: Model construction: The unit-autonomous distribution network optimization planning model is constructed with the power supply capacity, the total length of the new line, the total number of switches installed and the self-balancing of supply and demand as the objective function. The specific objective function is shown in the following formula: minP load -P DG |(12), where P i is the power supply capacity; w is the number of new lines; C i is the length of the i-th newly built line; β is the constant factor for converting switches into lines, that is, converting the investment of newly installed switches into the length of newly built lines with the same investment. This value is determined by the switch model and line model to be converted; Y is the total number of switches installed; P load is the load in the unit; P DG Provide power for the distributed power supply within the unit;

[0055] The present invention uses a node relationship matrix L to represent the optimized planning state of the network. L is a symmetrical 0-1 matrix. In order to avoid network complexity, the sum of the elements in each row or column cannot exceed 1, that is, each node is only connected to one other node; if an element in L is 1, a new line is created between the two nodes a and b represented by the row coordinates and column coordinates of the position of the element. The specific situations are as follows:

[0056] ① If the two nodes do not include the main transformer bus node, and there is no line between the two nodes; if node a is a node on a heavy-load line, then all nodes a and its downstream nodes are connected to node b;

[0057] ② If the two nodes do not include the main transformer bus node, and there is a line between the two nodes; if node a is a non-terminal node on the heavy-load line, then all nodes a and its downstream nodes are connected to node b, that is, the tie switch is changed to a section switch;

[0058] ③ If the two nodes do not include the main transformer bus node, and there is a line between the two nodes; if node a is the end node on the heavy-load line, find a suitable position to set it as a tie switch so that the load rates of the two lines are similar, and give priority to using the original section switch. If no suitable section switch is available, add a new switch;

[0059] ④ If one of the nodes is a main transformer bus node, let node a be the main transformer bus node, then node b is the exit node and the entire line is transferred to the node a transformer;

[0060] ⑤ If one of the two nodes is node a that is adjacent to the exit, and the load on the line of node b is not heavy, then node a and its downstream will all be transferred to node b to free up an exit line interval.

[0061] The constraints of the model are power constraints, voltage constraints, reliability constraints, transformer balance constraints and wind and solar power curtailment constraints.

[0062] Step 2.2: Model solution.

[0063] Specifically, the above-mentioned "unit autonomy" feeder network planning model considering the temporal and spatial complementarity of source and load is a multi-objective optimization problem. A more effective method to solve this problem is the non-dominated sorting genetic algorithm - an effective method for searching the Pareto frontier. The fitness function in the algorithm is composed of the objective function and the constraint violation penalty function, which is composed as follows: In the formula, F1(x i )、F2(x i ) are the fitness functions of maximum power supply capacity and economic investment respectively; f1(x i )、f2(x i ) are the maximum power supply capacity and economic investment respectively; v(x i ) is the total violation of the constraint condition; α1 and α2 are the penalty factors of the two objective functions, which are constants greater than 1 and their specific values ​​are determined by the value of the fitness function; if two individuals x a and x b satisfy: Then x a Dominate x b , otherwise they do not dominate each other; all individuals in the population that are not dominated by any other individuals constitute the Pareto solution set; the final decision plan is selected from this solution set.

[0064] In this embodiment, an example analysis is performed: In order to verify the effectiveness and applicability of the "cluster collaboration-unit autonomy" two-level planning model proposed in the present invention, the present invention uses the improved IEEE-33 node system as a basic example to carry out expansion planning of the distribution system; the planning period of the planning scheme is 10 years, and the average annual load growth level is 3%; the system Figure 2 As shown, its voltage level is 12.6kV, and the total initial active load demand and reactive load demand are 3715.0kW and 2300.0kvar respectively.

[0065] To meet the demand of load growth, all 32 lines in the original system can be upgraded and renovated. In addition, in order to meet the power supply needs of the newly added 5 nodes, the system has 12 lines to be built. Each line has three selection schemes. The relevant technical and economic parameters are shown in Table 1. Among the original lines, except for the line between node 1 and node 2 and the line between node 2 and node 3 with a current carrying capacity of 250A, the current carrying capacity of the remaining lines is 118A.

[0066] For the distributed wind power generation system, the unit capacity of the selected wind turbine is set to 100kW, and its cut-in wind speed, rated wind speed and cut-out wind speed are 3.5m / s, 12m / s and 25m / s respectively; for the distributed photovoltaic power generation system, the unit installed capacity is set to 50kW, and the selected battery array is a monocrystalline photovoltaic module, each module has a rated power of 280W, and the area of ​​each module is 1.6335m 2 The photovoltaic conversion efficiency of the components is 17.1%; the unit power of the energy storage system is set to 50kW, and the installation capacity can be selected to be 1-4 times of its power; the agreed electricity prices for wind power generation and photovoltaic power generation are 65 ($ / MWh) and 78 ($ / MWh) respectively, and the time-of-use electricity price for DS to purchase electricity from the superior power grid adopts the time-of-use electricity price data given above; the wind speed data, light intensity data and load demand data are the same as the data provided above.

[0067] Table 1 Technical and economic parameters of the selected lines

[0068]

[0069]

[0070] The technical and economic parameters of the renewable energy power generation system and energy storage system are shown in Table 2.

[0071] Table 2 Technical and economic parameters of the candidate renewable energy power generation system and energy storage system

[0072]

[0073] In order to achieve comparative analysis of calculation results, this embodiment mainly performs two example calculations to verify the effectiveness of the model and conduct comparative analysis, as shown in Table 3.

[0074] Table 3 Comparative example settings

[0075]

[0076] The optimization calculation is performed on the two given simulation examples, and a Pareto optimal planning scheme is selected in each example, as shown in Table 4; the network structures of the two planning schemes corresponding to Table 4 are as follows: Figure 3 and Figure 4 shown.

[0077] Table 4 The most balanced planning results in different examples

[0078]

[0079]

[0080] The present invention is a two-layer planning method for a local autonomous power grid based on net load analysis. The planning of the local autonomous power grid needs to fully consider the changes in the net load in the region. In use, the traditional planning method of the distribution network is difficult to cope with the situation where a large number of flexible resources are connected in a group in the local autonomous power grid. The present invention provides a method for power grid enterprises to make efficient decisions in the planning of the distribution network. The "cluster collaboration layer" planning in the two-layer planning model of the local autonomous power grid proposed by the present invention focuses on solving the problem of net load complementation and mutual assistance between stations, reducing the step-by-step return flow in the high-penetration distributed power source scenario, and the "unit autonomy layer" focuses on solving the feeder networking problem considering the temporal and spatial complementarity of sources and loads, realizing the local consumption of distributed power sources and minimizing the interactive energy between units, and finally obtaining a distribution network planning and construction plan with economic efficiency and safety that achieves Pareto optimality. The present invention adopts a non-dominated sorting genetic algorithm to solve multi-objective optimization problems, and has the advantages of good model stability, high solution efficiency, accurate results, etc. The present invention has the advantages of proposing a two-layer planning model, solving the problem of net load complementation and mutual assistance, reducing the step-by-step return flow, and realizing local consumption and minimizing the interactive energy.

Claims

1. A two-layer planning method for a local autonomous power grid based on net load analysis, characterized in that: The method comprises the following steps: Step 1: Construct a "cluster coordination" substation communication planning model that considers the net load complementarity between stations; Step 2: Establish a "unit autonomy" layer planning model that takes into account the temporal and spatial complementarity of sources and loads.

2. A two-layer planning method for a local autonomous power grid based on net load analysis as claimed in claim 1, characterized in that: The step 1 specifically comprises the following steps: Step 1.1: Construction of objective function; Step 1.2: Generation of variables in the main transformer connection relationship matrix; Step 1.3: Establishment of global and local inter-station interconnection equilibrium constraints of the main transformer: Step 1.4: Establishment of constraint conditions for the total number of communication channels between main transformer stations; Step 1.5: Establishment of net load constraint conditions within the power supply cluster: The net load constraint conditions within the power supply cluster should be as follows: P ≥ 0 (8), where: P is the net load of the cluster; Step 1.6: Solve the algorithm.

3. A two-layer planning method for a local autonomous power grid based on net load analysis as claimed in claim 2, characterized in that: The construction of the objective function in step 1.1 is specifically as follows: in combination with actual needs, the four objective functions that meet the requirements of maximum power supply capacity, minimum number of communication channels, minimum total length of communication channels, and minimum peak-to-valley difference of net load of power supply cluster are: maxpsc[L](1), min(P max -P min )(4), where L is the symmetric matrix of the main transformer connection relationship, L ij Indicates the connection relationship between main transformers No. i and No. j; N Σ is the total number of regional main transformers; d ij P is the length of the communication channel between main transformer stations; max is the maximum net load; P min is the minimum net load value.

4. A two-layer planning method for a local autonomous power grid based on net load analysis as claimed in claim 2, characterized in that: The generation of variables in the main transformer contact relationship matrix in step 1.2 is specifically as follows: the generation rule of variables in the main transformer contact relationship matrix is ​​as follows: Where, d lim The upper limit of the length of the communication channel allowed to be established between the main transformers of the substation; const is a constant 0 or 1.

5. The method for two-layer planning of a local autonomous power grid based on net load analysis according to claim 2, characterized in that: The establishment of the global and local inter-station communication equilibrium constraint conditions of the main transformer in step 1.3 is specifically as follows: from the perspective of the regularity and equilibrium of the inter-station communication of the main transformer of the substation, the established inter-station communication equilibrium constraint conditions of the main transformer are shown in the following formula: Where N Σ N is the total number of main transformers in all substations in the power supply area; (b)Σ 、N (b-1)Σ are the total number of main transformers of the first b and first b-1 substations in the power supply area; L if , L jf They represent the communication relationship between main transformers No. i and No. j and No. f respectively; δ1 is the balance constraint of the number of global communication channels between main transformers at the same station in the power supply area; δ2 is the balance constraint of the number of inter-station communication channels between the main transformer at the same station in a certain substation and any other substation; It is the set of main transformers within the ath substation in the power supply block.

6. A two-layer planning method for a local autonomous power grid based on net load analysis as claimed in claim 2, characterized in that: The establishment of the constraint condition of the total number of communication channels between main transformer stations in step 1.4 is specifically as follows: the constraint condition of the total number of communication channels between main transformer stations of any substation in the power supply area is as follows: Where N Σ is the total number of regional main transformers; L ij Indicates the connection relationship between main transformers No. i and No. j; D s The upper limit of the total number of connections between main transformer stations; It is the set of main transformers within the ath substation in the power supply block.

7. A two-layer planning method for a local autonomous power grid based on net load analysis as claimed in claim 2, characterized in that: The solution algorithm in step 1.6 specifically includes the following steps: Step 1.61: Calculation scale analysis of the optimization model of the main transformer station connection structure: The main transformer connection relationship matrix L can be expressed as a block matrix with the connection between each main transformer in the substation as the unit. Since L is a symmetric matrix, except for the case of the in-station connection structure, only the variables in the upper triangle or lower triangle of the matrix need to be analyzed. The connection variables L to be selected between the main transformer stations are: ij The number of is: In the formula, λ(L ij ) represents the number of interconnection variables to be selected between main transformer stations; n represents the number of transformer substations in the study area; N p 、N q Respectively represent the number of main transformers in the p-th and q-th substations; Step 1.62: Solution of the multi-objective optimization model of the main transformer interconnection structure based on genetic algorithm: Genetic algorithm is used to solve the model, and the theoretical maximum power supply capacity value of the power supply area under different interconnection structures is used as the fitness standard of genetic evolution. A sorting solution strategy is adopted for the multi-objective optimization problem, namely: meet certain power supply capacity requirements → the number of interconnection channels required in the region is the least → the total length of the regional interconnection channels is the shortest.

8. The method for two-layer planning of a local autonomous power grid based on net load analysis according to claim 1, characterized in that: The step 2 specifically includes the following steps: Step 2.1: Model construction; Step 2.2: Model solution.

9. A two-layer planning method for a local autonomous power grid based on net load analysis as claimed in claim 8, characterized in that: The model construction in step 2.1 is specifically as follows: a unit-autonomous distribution network optimization planning model is constructed with the power supply capacity, the total length of the new line, the total number of switches installed and the self-balancing of supply and demand as the objective function. The specific objective function is shown in the following formula: min|P load -P DG |(12), where P i is the power supply capacity; w is the number of new lines; C i is the length of the i-th newly built line; β is the constant factor for converting switches into lines; Y is the total number of switches installed; P load is the load in the unit; P DG Provides power for the distributed power supply within the unit.

10. A two-layer planning method for a local autonomous power grid based on net load analysis as claimed in claim 8, characterized in that: The model solution in step 2.2 is specifically: using a non-dominated sorting genetic algorithm, which is an effective method for searching the Pareto frontier, to solve the multi-objective optimization model of the "unit autonomy" feeder network planning considering the spatiotemporal complementarity of source and load. The fitness function in the algorithm is composed of an objective function and a constraint violation penalty function, which is composed as follows: In the formula, F1(x i )、F2(x i ) are the fitness functions of maximum power supply capacity and economic investment respectively; f1(x i )、f2(x i ) are the maximum power supply capacity and economic investment respectively; v(x i ) is the total violation of the constraint condition; α1 and α2 are the penalty factors of the two objective functions respectively; if two individuals x a and x b satisfy: Then x a dominatex b , otherwise they do not dominate each other; all individuals in the population that are not dominated by any other individuals constitute the Pareto solution set; the final decision plan is selected from this solution set.

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