Power distribution network power line and distributed power supply multi-target joint planning system and method

Abstract

The invention relates to a power grid planning technology, in particular to a power distribution network power line and distributed power supply multi-target joint planning system and method. The power distribution network power line and distributed power supply multi-target joint planning system comprises a case import and parameter setting module, a system integration module, a database module, a data processing module, an intelligent algorithm solving module, a multi-target value calculation module and a calculation result display module. The multi-target value calculation module comprises a double-layer model composed of an upper-layer model and a lower-layer model, and the upper-layer model and the lower-layer model respectively comprise different target functions. The upper-layer model and the lower-layer model are solved by using an intelligent algorithm mainly based on a modified multi-target gold washing optimization algorithm, the overall optimization of the joint planning scheme is realized, the obtained planning scheme can accurately isolate a fault line part when the power distribution network has a fault, a distributed power supply with a stable voltage level is provided for an isolated area, and the power distribution network fault planning method is suitable for the power distribution network. And the reliability and the recovery capability after a fault of the power distribution system are effectively improved.

Description

Technical Field

[0001] The present invention relates to a power grid planning technology, in particular to a multi-objective joint planning system and method for power distribution network power lines and distributed power sources. Background Art

[0002] To meet the expectations of key users for distribution network reliability and power quality, improving the system's ability to withstand disturbances is imperative. This requires accurate isolation of faulted lines and the provision of distributed power sources with stable voltage levels. This ensures high voltage quality in isolated areas during the restoration phase. Based on these requirements, improving distribution system reliability at the planning level has become a key research and development topic for power planning departments. However, limited publicly available documentation exists on this topic. Summary of the Invention

[0003] In response to the problems raised in the background technology, the purpose of the present invention is to provide a multi-objective joint planning system and method for distribution network power lines and distributed power sources. Through the system and method of the present invention, the planning scheme obtained is highly reliable, and when a distribution network fault occurs, the faulty line part can be accurately isolated, and a distributed power source with a stable voltage level can be provided to the isolated area. The present invention can effectively improve the reliability and post-fault recovery capability of the distribution system.

[0004] To achieve the above objectives, the present invention specifically adopts the following technical solutions:

[0005] A multi-objective joint planning method for power lines and distributed power sources in a distribution network comprises the following steps:

[0006] S1. The user imports information data of the planned distribution network into the multi-objective joint planning system for power lines and distributed generation of the distribution network; the information data of the planned distribution network includes grid topology information, load information, circuit breaker information, fuse information, and disconnector information;

[0007] S2. Set system parameters; system parameters include failure rate information, disconnector parameters, circuit breaker parameters, fuse parameters, and distributed power supply parameters;

[0008] S3. The system integration module automatically generates multiple simulated fault scenarios based on the information data and model parameters of the planned distribution network, and obtains a simulated fault data set corresponding to each simulated fault scenario through continuous interaction with the database module;

[0009] S4. The database module outputs the simulated fault data set to the data processing module, which processes the data;

[0010] S5. The intelligent algorithm solver solves the two-layer model of the multi-objective value calculation module based on the processed data. The objective function of the lower layer model evaluates the number of distributed power generation devices required to meet the voltage quality requirements of the isolated area. The upper layer model improves the reliability of the distribution system by adding new disconnectors, connecting lines, and distributing mobile energy storage systems.

[0011] S6. The intelligent algorithm solving module continuously obtains and measures the target values by solving the double-layer model of the multi-objective value calculation module, and uses an iterative method to generate and optimize the planning scheme, and finally obtains the optimal planning scheme. The multi-objective value calculation module outputs the optimal planning scheme and displays it to the user through the calculation result display module.

[0012] In step S1, the multi-objective joint planning system for distribution network power lines and distributed power sources includes a case import and parameter setting module, a system integration module, a database module, a data processing module, an intelligent algorithm solving module, a multi-objective value calculation module and a calculation result display module, wherein the case import and parameter setting module is communicatively connected to the system integration module, and a user imports the distribution network information to be planned and sets the model parameters to the system integration module; the system integration module is interactively connected to the database module, and the system integration module and the database module continuously interact to obtain a data set; the multi-objective value calculation module includes a two-layer model consisting of an upper model and a lower model, and the upper model and the lower model respectively include different objective functions; the database module outputs the data set to the data processing module, and after being processed by the data processing module, it is transmitted to the intelligent algorithm solving module; the intelligent algorithm solving module uses an intelligent algorithm to solve the two-layer model of the multi-objective value calculation module based on the processed data, continuously obtains the target value through the multi-objective value calculation and continuously measures the target value, and uses an iterative method to generate and optimize the planning scheme, and finally obtains the optimal planning scheme, and the multi-objective value calculation module outputs the optimal planning scheme, which is displayed to the user through the calculation result display module.

[0013] In step S1, the order of importing information data of the planned distribution network is as follows: S1.1, importing grid topology information; S1.2, importing load information; S1.3, importing circuit breaker information; S1.4, importing fuse information; S1.5, importing disconnector information.

[0014] In step S2, the order of setting system parameters is as follows: S2.1, setting failure rate information; S2.2, setting disconnector parameters; S2.3, setting circuit breaker parameters; S2.4, setting fuse parameters; S2.5, setting distributed power supply parameters.

[0015] In step S3, multiple simulated fault scenarios are generated using the following method: based on the information data and system parameters of the planned distribution network, at least one power source point is retained, and the maximum active power of the power source point is adjusted to the sum of the maximum active power of each load point to simulate the situation where faults occur in different lines.

[0016] In step S4, data processing includes: generating a node-line association matrix, searching and sorting root nodes, and generating a power supply matrix.

[0017] In step S5, the objective function of the upper model includes reliability index, installation cost and unsupplied power, wherein the installation cost includes the cost of adding disconnectors, connecting lines and mobile energy storage systems.

[0018] In step S5, the objective function of the lower model includes average voltage deviation, power balance constraint, capacity limitation and load constraint.

[0019] The present invention has the following beneficial effects: the present invention adopts a two-layer optimization method, firstly evaluating the number of distributed power supply devices required to meet the voltage quality requirements of isolated areas through a lower-layer model; then, based on all possible line fault simulations, determining the load nodes for distributed power supply restoration, further improving the existing reliability assessment method, and finally formulating an upper-layer model that includes a reliability assessment method. The present invention achieves overall optimization of the joint planning scheme by solving the upper and lower-layer models using an intelligent algorithm based on a modified multi-objective gold panning optimization algorithm. The obtained planning scheme can accurately isolate the faulty line section when a distribution network fault occurs, and provide distributed power supplies with stable voltage levels for isolated areas, effectively improving the reliability and post-fault recovery capabilities of the distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a system architecture diagram of the present invention;

[0021] Figure 2 It is the flow chart for importing distribution network information data;

[0022] Figure 3 It is a flowchart of system parameter setting;

[0023] Figure 4 This is the architecture diagram of the multi-objective value calculation module;

[0024] Figure 5 This is the overall flow chart of the intelligent algorithm used in the present invention;

[0025] Figure 6 Flowchart for parameter setting of the intelligent algorithm adopted by the present invention;

[0026] Figure 7This is a flow chart of variable initialization of the intelligent algorithm adopted by the present invention.

[0027] Figure 8 This is a flow chart of congestion calculation using the intelligent algorithm adopted by the present invention.

[0028] Figure 9 This is a flow chart of the crossover operation in the intelligent algorithm adopted by the present invention.

[0029] Figure 10 This is a flow chart of the mutation operation in the intelligent algorithm adopted by the present invention.

[0030] Figure 11 It is a flow chart of the system operation of the present invention.

[0031] Figure 12 This is the topology diagram of the distribution network node system to be planned in Example 1.

[0032] Figure 13 This is the topology diagram of the distribution network node system after planning and improvement in Example 1. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments.

[0034] like Figure 1 As shown, the present invention first proposes a multi-objective joint planning system for distribution network power lines and distributed power sources, including a case import and parameter setting module, a system integration module, a database module, a data processing module, an intelligent algorithm solving module, a multi-objective value calculation module and a calculation result display module, wherein the case import and parameter setting module is communicatively connected to the system integration module, and the user imports the distribution network information to be planned and sets the model parameters to the system integration module; the system integration module is interactively connected to the database module, and the system integration module and the database module continuously interact to obtain a data set; the multi-objective value calculation module includes a two-layer model consisting of an upper model and a lower model, and the upper model and the lower model respectively include different objective functions; the database module outputs the data set to the data processing module, and after being processed by the data processing module, it is transmitted to the intelligent algorithm solving module; the intelligent algorithm solving module uses an intelligent algorithm to solve the two-layer model of the multi-objective value calculation module based on the processed data, continuously obtains the target value through the multi-objective value calculation and continuously measures the target value, and uses an iterative method to generate and optimize the planning scheme, and finally obtains the optimal planning scheme, which is output by the multi-objective value calculation module and displayed to the user through the calculation result display module.

[0035] The present invention also proposes a distribution network planning method using the above-mentioned distribution network power line and distributed power generation multi-objective joint planning system, comprising the following steps:

[0036] S1. The user imports information data of the planned distribution network into the multi-objective joint planning system for power lines and distributed generation of the distribution network; the information data of the planned distribution network includes grid topology information, load information, circuit breaker information, fuse information, and disconnector information;

[0037] S2. Set system parameters; system parameters include failure rate information, disconnector parameters, circuit breaker parameters, fuse parameters, and distributed power supply parameters;

[0038] S3. The system integration module automatically generates multiple simulated fault scenarios based on the information data and system parameters of the planned distribution network, and obtains a simulated fault data set corresponding to each simulated fault scenario through continuous interaction with the database module;

[0039] S4. The database module outputs the simulated fault data set to the data processing module, which processes the data;

[0040] S5. The intelligent algorithm solver solves the two-layer model of the multi-objective value calculation module based on the processed data. The objective function of the lower layer model evaluates the number of distributed power generation devices required to meet the voltage quality requirements of the isolated area. The upper layer model improves the reliability of the distribution system by adding new disconnectors, connecting lines, and distributing mobile energy storage systems.

[0041] S6. The intelligent algorithm solving module continuously obtains and measures the target values by solving the double-layer model of the multi-objective value calculation module, and uses an iterative method to generate and optimize the planning scheme, and finally obtains the optimal planning scheme. The multi-objective value calculation module outputs the optimal planning scheme and displays it to the user through the calculation result display module.

[0042] like Figure 2 As shown, in step S1, the order of importing information data of the planned distribution network is as follows: S1.1, importing grid topology information; S1.2, importing load information; S1.3, importing circuit breaker information; S1.4, importing fuse information; S1.5, importing disconnector information.

[0043] like Figure 3 As shown, in step S2, the order of setting system parameters is as follows: S2.1, setting failure rate information; S2.2, setting isolation switch parameters; S2.3, setting circuit breaker parameters; S2.4, setting fuse parameters; S2.5, setting distributed power supply parameters.

[0044] In step S3, multiple simulated fault scenarios are generated using the following method: based on the information data and model parameters of the planned distribution network, at least one power source point is retained, and the maximum active power of the power source point is adjusted to the sum of the maximum active power of each load point to simulate the situation where faults occur in different lines.

[0045] In step S4, data processing includes generating a node-line association matrix, searching and sorting root nodes, and generating a power supply matrix. Data processing belongs to the prior art and will not be described in detail here.

[0046] In step S5, the objective function of the upper model includes reliability index, installation cost and unsupplied power, wherein the installation cost includes the cost of adding disconnectors, connecting lines and mobile energy storage systems. The objective function used by the upper model of the present invention is as follows:

[0047] 1) Reliability indicators

[0048] One of the objective functions of distribution system reliability is EENS, and the specific formula is shown in formula (1).

[0049]

[0050] In the above formula: FIMs are used to calculate the power outage time T and the expected unsupplied energy EENS based on the Hadamard product operation, which includes the branch failure rate λ1, the load node power P, the load level set π, etc.

[0051] 2) Installation cost

[0052] One of the indicators representing the economic efficiency of the distribution system is the equipment installation cost, which includes the cost of installing disconnectors, connecting lines, and equipping mobile energy storage systems. The specific formula is shown in formula (2):

[0053] C=c D +c T +c M =N D ×s D +N T ×s T +N M ×s M (2)

[0054] Formula (2) represents the cost of improving the reliability of the distribution network, where c D ,c T and c M Represent the cost of installing disconnect switches, installing connecting lines, and equipping mobile energy storage systems, N D and s D Respectively represent the number and unit cost of newly added disconnectors; N T and s T Respectively represent the number and unit cost of newly added wire ropes; N M and s M represent the number and unit cost of newly added mobile energy storage systems respectively.

[0055] 3) No electricity supply

[0056] To ensure high power quality after a fault, one of the objectives of the upper-level model is to calculate the unpowered power of all load nodes under various fault conditions. The lower-level optimization model uses an off-the-shelf solver to calculate the unpowered power. Simultaneously, the objective function is Pareto-ranked along with the EENS and installation cost to determine the planning and scheduling strategy. The specific calculation formula is as follows:

[0057]

[0058] Equation (3) represents the minimization of the reactive power of the load node under all fault conditions, where Pnotsupply,i,t,s is the reactive power of node i at time t under fault condition s.

[0059] In step S5, the objective function of the lower model includes average voltage deviation, power balance constraint, capacity limit and load constraint. In the lower model proposed by the present invention, in order to ensure high voltage quality and less unpowered power after the fault, the goal of the lower model is to minimize the average voltage deviation of the load node during the power outage. At the same time, according to the optimization results, the unpowered power of all load nodes under various fault conditions can be obtained. In the upper model, the unpowered power of all load nodes is one of the objective functions of the Pareto sorting, as shown in formula (3). In addition, the MESS power supply strategy can also be obtained through the lower optimization model. Therefore, the specific objective function and constraints of the lower model are as follows:

[0060] 1) Lower-level model objective function

[0061] During the distribution system fault recovery phase, to ensure uninterrupted power supply to users, the MESS should promptly provide emergency power to isolated areas to minimize unpowered power at load nodes. Furthermore, to prevent voltage collapse, the average voltage deviation should be set as the objective function of the low-level model. This is calculated as follows:

[0062]

[0063] Formula (4) represents the average voltage deviation ΔV of all nodes under various line fault conditions after adding the mobile energy storage system. avg , N L and N B are the number of lines and nodes in the distribution system, S represents the set of line fault conditions in the distribution system, V i,t,s represents the voltage at node i at time t under line fault condition s.

[0064] 2) Power balance constraints

[0065] In grid operation, the injected power of each bus should be equal to its output power. The lower model uses the power flow balance constraint of second-order cone relaxation to ensure power balance.

[0066]

[0067] Expression (5) represents the active power balance and reactive power balance of the node with the participation of the mobile energy storage system. out ) and matrix input (matrix in ) represents the relationship between nodes and lines. Specifically, the matrix out represents the line matrix of the node flowing out of the reference direction, and matrix in P represents the line matrix flowing into the node in the reference direction. ij,t and Q ij,t is the active power and reactive power of line ij at time t, and Iij,t is equal to the square of the current in line ij. In addition, rij and xij are the resistance and reactance of line ij, respectively. P g,i,t and Q g,i,t are the active power and reactive power of source node i at time t. i,dch,t, and P i,ch,t They represent the MESS discharge active power and charging active power of node i at time t respectively. i,dch,t and Q i,ch,t Respectively represent the MESS discharge and charging reactive power of node i at time t. In addition, P load,i,t and Q load,i,t represent the active load and reactive load of node i at time t respectively.

[0068] 3) Capacity Limitation

[0069] In order to meet the thermal stability limit of the line and the limit of MESS charging and discharging power, constraints should be imposed on the power transmission of the line and the charging and discharging power of the energy storage.

[0070]

[0071] Formula (7) expresses the transmission capacity limitation and thermal stability limitation of the line, where S ij,max and I ij,max are the transmission capacity limitation and thermal stability of line ij, respectively. Formula (8) ensures that the charging and discharging power of MESS is limited to the maximum charging and discharging power range, that is, S ess_max In formula (8), o i,dch,t and o i,ch,t are Boolean variables representing the discharge and charge states of the MESS respectively.

[0072] 4) Load reduction constraints

[0073] When a power distribution system fails, the blackout area will be isolated, forcing some bus loads within the isolated area to be out of power or waiting for power from distributed power sources. If the distributed power sources are insufficient, the node voltage will also be affected. This paper establishes a bus load reduction constraint model, which is as follows:

[0074]

[0075] Expression (9) represents the static load model, where and are constant impedance, constant current and power fraction respectively, P load,i,base and Q load,i,base is the voltage v i,base The load active power and reactive power at the same speed.

[0076] In the present invention, the architecture of the multi-objective value calculation module is as follows Figure 4 As shown, it also includes a relevant data processing unit, which receives the data based on which the intelligent algorithm solves the double-layer model, so that the calculation result corresponds to the data based on which the intelligent algorithm solves the double-layer model.

[0077] In the present invention, the intelligent algorithm used in the intelligent algorithm solution module is an intelligent algorithm based on the modified multi-objective gold mining optimization algorithm. This algorithm is an existing technology, and its overall solution process is as follows: Figure 5 As shown in the figure, the algorithm parameter setting process is as follows Figure 6 As shown, the process of variable initialization in the algorithm is as follows Figure 7 As shown, the process of congestion calculation in the algorithm is as follows Figure 8 As shown, the process of crossover operation in the algorithm is as follows Figure 9 As shown, the process of mutation operation in the algorithm is as follows Figure 10 shown.

[0078] The system operation process of the present invention is as follows Figure 11 shown.

[0079] Example 1

[0080] Example 1, by simulation Figure 12 A real distribution network fault scenario is shown to validate the proposed approach. The simulation involves coordinating new disconnectors, connecting lines, and MESS during the optimization process.

[0081] The system consists of one power node, 14 load points, 13 lines, and two connecting lines. The system is equipped with 11 circuit breakers and two fuses to isolate faulty components. The fault frequency for each line is set at 0.1 per year, with a repair time of 8 hours, a circuit breaker operating time of 0.03 hours, and a fuse operating time of 0.01 hours. Power points can also serve as load points. The proposed distribution network is a variation of the IEEE 14-node standard system. To better verify fault scenarios compared to standard cases, the following settings are provided in Table 1.

[0082] Table 1: Changes in IEEE 14 standard node systems

[0083]

[0084] The joint planning method provided by the present invention is used to improve the grid configuration. The improved 14-node distribution network system is as follows: Figure 13 In the same fault scenario, the Pareto optimal solutions of the system objective function before and after optimization are shown in Table 2 below.

[0085] Table 2: Pareto optimal solution

[0086]

[0087] As can be seen from the above table, the reliability index of the improved grid configuration using the joint planning method provided by the present invention is better than that of the unoptimized index. Although the cost has increased, the cost increase is negligible for important isolated areas where high voltage quality must be guaranteed.

[0088] The parts not described in detail in this invention are prior art.

Claims

1. A multi-objective joint planning method for power lines and distributed generation in a distribution network, characterized by: The following steps are involved: S1. The user imports information data of the planned distribution network into the multi-objective joint planning system for power lines and distributed generation of the distribution network; the information data of the planned distribution network includes grid topology information, load information, circuit breaker information, fuse information, and disconnector information; S2. Set system parameters; system parameters include failure rate information, disconnector parameters, circuit breaker parameters, fuse parameters, and distributed power supply parameters; S3. The system integration module automatically generates multiple simulated fault scenarios based on the information data and system parameters of the planned distribution network, and obtains a simulated fault data set corresponding to each simulated fault scenario through continuous interaction with the database module; S4. The database module outputs the simulated fault data set to the data processing module, which processes the data; S5. The intelligent algorithm solver solves the two-layer model of the multi-objective value calculation module based on the processed data. The objective function of the lower layer model evaluates the number of distributed power generation devices required to meet the voltage quality requirements of the isolated area. The upper layer model improves the reliability of the distribution system by adding new disconnectors, connecting lines, and distributing mobile energy storage systems. S6. The intelligent algorithm solving module continuously obtains and measures the target values by solving the double-layer model of the multi-objective value calculation module, and uses an iterative method to generate and optimize the planning scheme, and finally obtains the optimal planning scheme. The multi-objective value calculation module outputs the optimal planning scheme and displays it to the user through the calculation result display module.

2. The multi-objective joint planning method for power lines and distributed generation in a distribution network according to claim 1 is characterized by: In step S1, the multi-objective joint planning system for distribution network power lines and distributed power sources includes a case import and parameter setting module, a system integration module, a database module, a data processing module, an intelligent algorithm solving module, a multi-objective value calculation module and a calculation result display module. The case import and parameter setting module is communicatively connected to the system integration module, and the user imports the distribution network information to be planned and sets the model parameters to the system integration module; the system integration module is interactively connected to the database module, and the system integration module and the database module continuously interact to obtain a data set; the multi-objective value calculation module includes a two-layer model consisting of an upper model and a lower model, and the upper model and the lower model respectively include different objective functions; the database module outputs the data set to the data processing module, and after being processed by the data processing module, it is transmitted to the intelligent algorithm solving module; the intelligent algorithm solving module uses an intelligent algorithm to solve the two-layer model of the multi-objective value calculation module based on the processed data, continuously obtains the target value through the multi-objective value calculation and continuously measures the target value, and uses an iterative method to generate and optimize the planning scheme, and finally obtains the optimal planning scheme, and the multi-objective value calculation module outputs the optimal planning scheme, which is displayed to the user through the calculation result display module.

3. The multi-objective joint planning method for power lines and distributed generation in a distribution network according to claim 1 is characterized by: In step S1, the order of importing information data of the planned distribution network is as follows: S1.1, importing grid topology information; S1.2, importing load information; S1.3, importing circuit breaker information; S1.4, importing fuse information; S1.5, importing disconnector information.

4. The multi-objective joint planning method for power lines and distributed generation in a distribution network according to claim 1 is characterized by: In step S2, the order of setting system parameters is as follows: S2.1, setting failure rate information; S2.2, setting disconnector parameters; S2.3, setting circuit breaker parameters; S2.4, setting fuse parameters; S2.5, setting distributed power supply parameters.

5. The multi-objective joint planning method for power lines and distributed generation in a distribution network according to claim 1 is characterized by: In step S3, multiple simulated fault scenarios are generated using the following method: based on the information data and system parameters of the planned distribution network, at least one power source point is retained, and the maximum active power of the power source point is adjusted to the sum of the maximum active power of each load point to simulate the situation where faults occur in different lines.

6. The multi-objective joint planning method for power lines and distributed generation in a distribution network according to claim 1, characterized in that: In step S4, data processing includes: generating a node-line association matrix, searching and sorting root nodes, and generating a power supply matrix.

7. The multi-objective joint planning method for power lines and distributed generation in a distribution network according to claim 1, characterized in that: In step S5, the objective function of the upper model includes reliability index, installation cost and unsupplied power, wherein the installation cost includes the cost of adding disconnectors, connecting lines and mobile energy storage systems.

8. The multi-objective joint planning method for power lines and distributed generation in a distribution network according to claim 1, characterized in that: In step S5, the objective function of the lower model includes average voltage deviation, power balance constraint, capacity limitation and load constraint.

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