Distributed photovoltaic bearing capacity assessment method and simulation system

Abstract

The invention provides a distributed photovoltaic bearing capacity assessment method and a simulation system, and belongs to the technical field of power distribution. Comprising the following two steps: (1) power distribution network new energy bearing capacity safety scoring based on multiple limitations inside and outside a sequence; and (2) carrying out distributed photovoltaic bearing capacity evaluation and simulation verification based on optimal safety and economic efficiency. According to the method, the accuracy of the new energy bearing capacity safety score of the power distribution network is improved, and the fluctuation coping capacity of the distributed photovoltaic bearing capacity assessment method is improved.

Description

Technical Field

[0001] The present invention provides a distributed photovoltaic carrying capacity evaluation method and a simulation system, belonging to the technical field of power distribution. Background Art

[0002] As a flexible, clean and renewable energy source, distributed photovoltaics have become an important means to solve energy shortages and reduce carbon emissions with their advantages of strong flexibility and small impact on the power grid. Distributed photovoltaic carrying capacity refers to the maximum capacity of the distribution network that can accommodate distributed photovoltaic power generation devices under the premise of meeting safe, stable and economical operation. In the planning and operation of the power system, the carrying capacity assessment of the distribution network is the key to ensuring the safety, stability and economic operation of the power grid. With the continuous expansion of the scale of distributed photovoltaic power generation devices, the original transformers may not be able to meet the increased power load, and higher technical requirements are put forward for the distribution network area. When the photovoltaic power generation power exceeds the load power of the grid connection point, it may cause power backflow and raise the voltage of the photovoltaic grid connection point, thereby causing problems such as voltage over-limit. At the same time, the imbalance between high photovoltaic power generation power and low load demand is prone to power flow backflow, which in turn leads to problems such as line overvoltage. In addition, when high-penetration distributed photovoltaics are connected to the low-voltage distribution network, substations and lines are prone to heavy load or overload due to photovoltaic power flow backflow. Therefore, how to scientifically and reasonably evaluate its carrying capacity for the distribution network has become a major problem in power system planning and scheduling.

[0003] Accurately assessing the carrying capacity of photovoltaic systems for the power grid will help identify possible safety risks that the power grid may encounter, and take targeted measures to regulate and optimize it to ensure the stable operation of the power system. Through scientific carrying capacity assessment, it can provide a basis for the optimal configuration of photovoltaic systems, and reasonably select equipment capacity, energy storage configuration and scheduling strategies, so as to achieve efficient utilization of photovoltaic power generation, avoid excessive construction and waste of resources, and improve the overall efficiency of the system. In the electricity market, a reasonable assessment of photovoltaic carrying capacity can also help optimize the electricity price mechanism, promote the consumption of new energy, and promote the market-oriented operation of distributed photovoltaics. Therefore, developing an assessment method that can both ensure system safety and optimize economic benefits has become the key to achieving efficient and stable operation of distributed photovoltaic power generation. However, economical and reliable distributed photovoltaic carrying capacity assessment still needs to solve the following technical challenges:

[0004] First, the traditional distributed photovoltaic carrying capacity assessment method is difficult to simultaneously consider the combined situation of multiple performance sequences including three-phase imbalance, transmission load rate of the substation, reverse load rate of the distribution transformer, voltage deviation, and the impact of extreme conditions such as bad weather and equipment failure on the operation of the distribution network, resulting in insufficient accuracy in the assessment process and failure to fully reflect the complexity of actual operation. When dealing with multiple limiting factors inside and outside the sequence, the traditional optimization method cannot fully evaluate the performance fluctuations under different schemes, resulting in inaccurate carrying capacity assessment results, affecting the reasonable configuration and application of energy storage and transformers.

[0005] Second, traditional distributed photovoltaic carrying capacity assessment methods are often unable to comprehensively optimize multiple objectives such as safety, transformer expansion costs, and distributed photovoltaic open capacity, resulting in the assessment results being unable to effectively guide the selection of actual solutions and making it difficult to balance safety, economic efficiency, and so on. Therefore, consideration is given to introducing a distributed photovoltaic carrying capacity assessment method with the best safety and economic efficiency to ensure that the final assessment results can meet both safety and economic requirements.

[0006] Therefore, there is an urgent need to design an economical and reliable distributed photovoltaic carrying capacity assessment method and simulation system. Summary of the invention

[0007] The present invention provides a distributed photovoltaic carrying capacity evaluation method and simulation system to solve the following two technical problems:

[0008] 1. How to effectively integrate multiple constraints within and outside the sequence with the user-side demand management model to evaluate the distributed photovoltaic carrying capacity;

[0009] It is difficult in existing technical solutions to simultaneously consider the combined situation of multiple performance sequences including three-phase imbalance, transmission load rate of the substation, reverse load rate of the distribution transformer, voltage deviation, and the impact of extreme situations on the operation of the distribution network, resulting in insufficient accuracy in the evaluation process and failure to fully reflect the complexity of actual operation. When dealing with multiple constraints inside and outside the sequence, traditional optimization methods are unable to fully evaluate the performance fluctuations under different schemes, resulting in inaccurate results of the carrying capacity evaluation, affecting the reasonable configuration and application of energy storage and transformers. Therefore, how to effectively integrate multiple constraints inside and outside the sequence to evaluate the distributed photovoltaic carrying capacity is an issue that needs to be considered urgently.

[0010] 2. How to achieve a balance between safety and economy in the evaluation of distributed photovoltaic carrying capacity;

[0011] Existing technical solutions often fail to comprehensively optimize multiple goals such as safety, transformer expansion costs, and distributed photovoltaic open capacity, resulting in the inability of evaluation results to effectively guide the selection of actual solutions, making it difficult to balance safety, economic efficiency, and ensuring that the final evaluation results can meet both safety and economic requirements. Therefore, how to calculate and select the optimal solution to ensure system safety and achieve maximum economic benefits has become a key issue that new evaluation methods need to solve.

[0012] The complete technical solution provided by the present invention is:

[0013] A distributed photovoltaic carrying capacity assessment method includes the following two steps:

[0014] (1) Security scoring of the new energy carrying capacity of the distribution network based on multiple constraints inside and outside the sequence;

[0015] (2) Evaluation and simulation verification of distributed photovoltaic carrying capacity based on optimal safety, economic efficiency;

[0016] Specific:

[0017] (1) The safety scoring of the new energy carrying capacity of the distribution network based on multiple constraints inside and outside the sequence includes the following processes:

[0018] Firstly, a distributed photovoltaic output and load power data set is constructed and divided into an optimization data set and a verification data set and stored in the database.

[0019] Secondly, select the input parameter generation mode to input the time and energy storage configuration to be evaluated into the simulation system.

[0020] Then, the simulation system uses the optimization data set to perform multi-layer iterative optimization, in which the innermost layer optimizes energy storage charging and discharging, the middle layer optimizes transformer expansion, and the outermost layer optimizes energy storage configuration, and records the transformer expansion cost, photovoltaic open capacity and related performance sequences under different configurations.

[0021] Finally, based on the fluctuation of the performance sequence and the internal and external restrictions of the sequence, it is determined whether to conduct a safety score with multiple restrictions inside and outside the sequence. If the performance sequence has extreme or multiple severe situations, or multiple performance sequences have non-ideal situations, the scheme is regarded as a restricted scheme and no score is given. For other schemes to be adopted, multiple restrictions inside and outside the sequence are considered, and a safety score with multiple restrictions inside and outside the sequence is conducted. Based on the relationship between the restriction scores inside different sequences, the adaptive weight coefficients of different performance sequences are adjusted according to the evaluation rules of the restriction scores outside the sequence. On the basis of calculating the safety scores of the internal and external restrictions of different performance sequences, the comprehensive safety score of the scheme with multiple restrictions inside and outside the sequence is finally obtained.

[0022] (2) Evaluation and simulation verification of distributed photovoltaic carrying capacity based on optimal safety, economic efficiency, including the following processes:

[0023] Firstly, based on the safety scores of multiple restrictions inside and outside the comprehensive sequence of the scheme, the transformer expansion cost and the open capacity of distributed photovoltaics, the safety and economic efficiency of the distributed photovoltaic carrying capacity of the distribution network is calculated, and the scheme with the best combination of safety and economic efficiency and distributed photovoltaic open capacity is selected as the final optimization scheme.

[0024] Secondly, based on the configuration scheme and the distributed PV carrying capacity assessment results, the simulation system uses the validation data set to perform simulations and record the corresponding performance sequences.

[0025] Finally, a safety score is given to the final optimization scheme selected. If the difference between this score result and the previous score result of this scheme is greater than the threshold, or multiple restrictions occur, the carrying capacity needs to be re-evaluated.

[0026] A distributed photovoltaic carrying capacity evaluation simulation system includes a database, a simulation scenario generation module, a simulation optimization module, a sequence internal and external multiple restriction safety score calculation module, a distributed photovoltaic carrying capacity safety and economic efficiency calculation module and a final optimization solution determination module.

[0027] in:

[0028] The database is used to store distributed photovoltaic output and load power data sets, including optimization and verification data sets;

[0029] The simulation scenario generation module uses the stored data in the database and adopts two modes to generate distributed photovoltaic and load simulation scenarios;

[0030] The simulation optimization module is used to simulate based on the input distributed photovoltaic and load simulation scenarios, use the optimization data set to perform simulation, and perform multi-layer iterative optimization to obtain the optimal transformer capacity and distributed photovoltaic capacity, and record the corresponding performance sequence, or simulate based on the energy storage and transformer configuration scheme, distributed photovoltaic carrying capacity assessment results and verification data set, and record the corresponding performance sequence;

[0031] The sequence internal and external multiple restriction safety score calculation module is used to perform sequence internal and external multiple restriction safety scores for different energy storage and transformer configuration schemes based on performance sequence fluctuations and sequence internal and external restrictions;

[0032] The distributed photovoltaic carrying capacity safety and economic efficiency calculation module is used to calculate the safety and economic efficiency of the distributed photovoltaic carrying capacity of the distribution network based on the safety score, transformer expansion cost, and distributed photovoltaic open capacity, and assist in simulation optimization to obtain an optimization plan;

[0033] The final optimization scheme determination module performs simulation verification based on the calculated sequence internal and external limit safety scores, and outputs the evaluation results and the final optimization scheme. The simulation system improves the convenience and accuracy of distributed photovoltaic carrying capacity evaluation.

[0034] The simulation system can support two modes to generate simulation scenarios. The first mode is to select the distributed photovoltaic output data and load power data on day T from the database for scenario generation. The second mode generates the distributed photovoltaic and load power curves on day T based on the key parameters of the probability distribution function of distributed photovoltaic output and load power, and generates scenarios.

[0035] The specific technical effects of the present invention are:

[0036] (1) The present invention proposes a method for scoring the safety of the new energy carrying capacity of a distribution network based on multiple constraints inside and outside the sequence, thereby improving the accuracy of the safety scoring of the new energy carrying capacity of the distribution network.

[0037] (2) The present invention proposes a distributed photovoltaic carrying capacity assessment and simulation verification method based on optimal safety, economic efficiency, which improves the ability of the distributed photovoltaic carrying capacity assessment method to cope with fluctuations.

[0038] (3) The present invention proposes a distributed photovoltaic carrying capacity assessment simulation system, which improves the convenience and accuracy of distributed photovoltaic carrying capacity assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of the economical and reliable distributed photovoltaic carrying capacity assessment method of the present invention;

[0040] Figure 2 Schematic diagram of the distributed photovoltaic carrying capacity assessment simulation system of the present invention. DETAILED DESCRIPTION

[0041] The technical solution proposed in this invention mainly includes the following two steps: (1) safety scoring of the new energy carrying capacity of the distribution network based on multiple constraints inside and outside the sequence; (2) evaluation and simulation verification of the distributed photovoltaic carrying capacity based on optimal safety, economic efficiency. Figure 1 As shown. Specifically:

[0042] 1. Safety score of new energy carrying capacity of distribution network based on multiple constraints inside and outside the sequence;

[0043] Considering that there are I nodes in the distribution network, the set is expressed as , where i represents the i-th node. The simulation system can be set to evaluate the distributed photovoltaic carrying capacity on day T, and the set is expressed as , where t represents the tth day.

[0044] Among them, the present invention proposes a distributed photovoltaic carrying capacity assessment simulation system, such as Figure 2 As shown, the database is used to store distributed photovoltaic output and load power data sets, including optimization and verification data sets; the simulation scenario generation module uses the stored data in the database to generate distributed photovoltaic and load simulation scenarios in two modes. The simulation optimization module is used to simulate based on the input distributed photovoltaic and load simulation scenarios using the optimization data set, and multi-layer iterative optimization to obtain the optimal transformer capacity and distributed photovoltaic capacity, and record the corresponding performance sequence, or simulate based on the energy storage and transformer configuration scheme, distributed photovoltaic carrying capacity assessment results and verification data set, and record the corresponding performance sequence; the sequence internal and external multiple restriction safety score calculation module is used to perform sequence internal and external multiple restriction safety scores for different energy storage and transformer configuration schemes according to performance sequence fluctuations and sequence internal and external restrictions; the distributed photovoltaic carrying capacity safety and economic efficiency calculation module is used to calculate the distribution network distributed photovoltaic carrying capacity safety and economic efficiency based on safety scores, transformer expansion costs, and distributed photovoltaic open capacity, and assist in simulation optimization to obtain optimization solutions; the final optimization solution determination module performs simulation verification based on the calculated sequence internal and external restriction safety scores according to the dotted line path in the figure, and outputs the evaluation results and the final optimization solution.

[0045] The simulation system can support two modes to generate simulation scenarios. The first mode is to select the distributed photovoltaic output data and load power data on day T from the database for scenario generation. The second mode generates the distributed photovoltaic and load power curves on day T based on the key parameters of the probability distribution function of distributed photovoltaic output and load power, and generates scenarios.

[0046] For the first mode, based on the historical operation data of the distribution network and the daily distributed photovoltaic output data of the distribution network, a distributed photovoltaic output data set is constructed, including the historical photovoltaic output data of the distribution network. Similarly, based on the daily load power data of the distribution network, a load power data set is constructed, including the historical load power of the distribution network. For the distributed photovoltaic output data set and the load power data set, they are merged to form an optimized data set, and a copy is made to form a verification data set to complete the simulation scenario generation.

[0047] For the second mode, based on the historical operation data of the distribution network, the key parameters of the distributed photovoltaic and load probability distribution models in different seasons are set, and the corresponding distributed photovoltaic and load curves are generated according to actual needs to generate the corresponding simulation scenarios.

[0048] Assuming that the source, load and storage are dispatched every 15 minutes, there are 96 dispatches in one day. For the 96 storage dispatches on day t, the set is expressed as where h t is the hth day of the tth day tThe index of the sub-scheduling, H t =96. The change of electricity quantity during energy storage dispatch is expressed as:

[0049]

[0050] In the formula, is the hth energy storage station at node i on day t t +1 energy storage capacity during dispatch, is the hth energy storage station at node i on day t t Energy storage capacity at the time of dispatch, η ch and η dis are the energy storage charging and discharging efficiency, τ is the scheduling time scale, i.e. 15 minutes, and They are the hth energy storage station at node i on day t. t The energy storage charging and discharging power during the next dispatch needs to meet the following constraints:

[0051]

[0052] In the formula, It is a variable that characterizes the charging and discharging state of energy storage, 1 represents charging, 0 represents discharging, P ES is the rated power of a single energy storage module, The number of energy storage modules configured for the energy storage power station at node i is a parameter manually input into the simulation system and is fixed in each simulation.

[0053] The constraints that the energy storage capacity must satisfy are expressed as:

[0054]

[0055] In the formula, E ES is the rated capacity of a single energy storage module, and The upper and lower limits of energy storage are stored.

[0056] For a simulation, the transformer capacity is initialized to V0, and the distributed photovoltaic capacity is initialized to Q0. Based on the optimization data set, multi-layer iterative optimization is performed. The specific process is: first, the inner layer optimization is performed, that is, the optimization of energy storage charging and discharging scheduling. The optimization target is to maximize the distributed photovoltaic access capacity. The optimization variable is the charging and discharging power of the energy storage power station. The constraints are formulas (2) and (3). Secondly, the middle layer optimization is performed, that is, the transformer expansion configuration optimization. The optimization target is the transformer expansion cost. The optimization variable is the transformer capacity, which must meet the constraints of short-circuit current and multiple safety score restrictions inside and outside the sequence. It will be introduced in detail in the next part. The outer layer is the comprehensive configuration optimization of the distribution network. The optimization target is the joint optimization of the safety and economic efficiency of the distributed photovoltaic carrying capacity and the open capacity of the distributed photovoltaic. The optimization variables are the configuration schemes of energy storage and transformers. It will be introduced in detail in the innovation point 2 of the present invention. The optimization problem can be expressed as:

[0057]

[0058] Where, Q is the distributed photovoltaic access capacity, Q k is the distributed photovoltaic access capacity of scheme k, C V is the transformer expansion cost, V i is the transformer configuration capacity of the i-th node. k is the k-th energy storage and transformer configuration scheme, η(k) is the safety and economic efficiency of distributed photovoltaic carrying capacity, Φ4 and Φ5 are weight coefficients, expressed as:

[0059]

[0060] Where C0 is the cost of expanding the unit capacity of the transformer.

[0061] By solving the above optimization problem, the simulation system generates the distributed photovoltaic capacity, transformer capacity and transformer expansion cost when the distributed photovoltaic capacity reaches the optimal value under the current energy storage configuration.

[0062] The outer transformer expansion optimization constraint is the short-circuit current constraint, that is:

[0063] I xz <I m (6)

[0064] In the formula, I xz The busbar short-circuit current data generated for the simulation system, I m is the allowable short-circuit current limit.

[0065] For each simulation under different inner optimizations, the simulation system records the performance sequences of three-phase imbalance, load rate of the substation, reverse load rate of the distribution transformer, voltage deviation, etc. during each energy storage dispatch every day. The set of solutions for different energy storage and transformer configurations is defined as Where K is the total number of schemes. The three-phase unbalance sequence under the kth scheme is expressed as in is the kth plan, the hth day on the tth day t The three-phase imbalance degree during the next dispatch. The load rate sequence of the substation area is expressed as in is the kth plan, the hth day on the tth day t The load rate of the transmission area during the next dispatch. The reverse load rate sequence of the distribution transformer is expressed as in is the kth plan, the hth day on the tth day t The reverse load rate of the distribution transformer during the next dispatch. The voltage deviation sequence is expressed as in is the kth plan, the hth day on the tth day t Voltage deviation during the next dispatch.

[0066] Finally, multiple safety scores are scored for different energy storage and transformer configurations. Specifically, for the above performance sequences, if the sequence fluctuation exceeds the threshold, the sequence fluctuation is considered to be too large, which is expressed as:

[0067] D[Y]>D Y,th (7)

[0068] In the formula, Y represents any performance sequence, D[·] is the variance function, and D Y,th is the fluctuation threshold of performance sequence Y.

[0069] For the load rate sequence of the transmission area, if or It is considered that the sequence has an extreme case. or It is considered that the sequence has a serious situation, among which, represents the transmission load rate sequence of the substation or the reverse load rate sequence of the distribution transformer on the tth day and the hth day under any scheme. t The value of the next scheduling, y omin represents the extreme lower threshold, y omax represents the extreme upper threshold, y emin Indicates the severe lower threshold, y emax represents the severe upper threshold, and y emin >y omin ,y emax <y omax .

[0070] For the distribution transformer reverse load rate sequence, three-phase unbalance sequence and voltage deviation sequence, if It is considered that the sequence has an extreme case. It is considered that the sequence has a serious situation. At this time, Represents the three-phase unbalance sequence and voltage deviation sequence under any scheme at the hth day of the tth day t The value of the next scheduling. Excessive sequence fluctuations, extreme cases, and severe cases are considered non-ideal.

[0071] For a solution with different energy storage and transformer configurations, if there is a single performance sequence with one extreme situation or multiple severe situations, that is, or or or Among them, ζ is the specified tolerable threshold of severe situations, or if multiple sequences have multiple non-ideal situations, then the scheme is a restricted scheme, and the scheme is not selected as a potential option and is not scored.

[0072] For each potential solution, multiple safety restrictions inside and outside the sequence need to be scored. For each performance sequence, multiple restrictions inside and outside the sequence need to be considered to calculate the sequence score. Taking the three-phase imbalance sequence as an example, the restriction score inside the sequence is k,δ It is expressed as:

[0073]

[0074] In the formula, γ D is the influence factor of the intra-sequence restriction score fluctuation, γ is the influence factor of the intra-sequence restriction score sequence performance, δ smax is the upper limit of the three-phase imbalance. The limit scores in the sequence are divided into three levels from high to low, namely, level one, level two and level three, according to the grading rules preset by the simulation system.

[0075] In addition to considering the performance of the three-phase imbalance sequence itself, the external restrictions of the sequence, that is, the performance of other sequences, should also be considered. Therefore, it is necessary to first calculate the restriction scores of other sequences, recorded as Score k,θ 、Score k,σ , The rule for evaluating the out-of-sequence restriction score is as follows: for the case where the in-sequence restriction score of the three-phase imbalance degree sequence is level one or level two, if the in-sequence restriction scores of other sequences are not lower than level two, then the internal and external multiple restriction safety score of the three-phase imbalance degree sequence is higher, otherwise the internal and external multiple restriction safety score of the three-phase imbalance degree sequence is lower; for the case where the in-sequence restriction score of the three-phase imbalance degree sequence is level three, if at least one sequence has a level one in-sequence restriction score, then the internal and external multiple restriction safety score of the three-phase imbalance degree sequence is higher, otherwise the internal and external multiple restriction safety score of the three-phase imbalance degree sequence is lower. The internal and external multiple restriction safety score of the three-phase imbalance degree sequence is expressed as:

[0076]

[0077] In the formula, ω k,δ ,ω k,θ ,ω k,σ , They are the adaptive weight coefficients of the three-phase unbalance sequence, the transmission load rate sequence of the substation, the reverse load rate sequence of the distribution transformer, and the voltage deviation sequence, which are related to the intra-sequence restriction score level of each sequence under this scheme. The size relationship between them follows the rules mentioned above. The above case can be expressed as:

[0078]

[0079] In the formula, ω0 is the weight growth rate, Score k,δ For level three, if Score k,θ is first order, then ω k,θ It is much larger than other weight coefficients, and the internal and external multiple limit safety score of the three-phase unbalance sequence is higher.

[0080]

[0081] In the formula, Score k,δ If the limit scores in other sequences are all at level two or level three, the weight coefficients are similar in size, and the internal and external multiple limit safety scores of the three-phase imbalance sequence are low.

[0082]

[0083] In the formula, Score k,δ If the limit scores in other sequences are all at level one or level two, the weight coefficients are similar in size, and the internal and external multiple limit safety scores of the three-phase unbalance sequence are higher.

[0084]

[0085] In the formula, Score k,δ If the score is level 1 or level 2, k,θ is level three, then ω k,θ It is much larger than other weight coefficients, and the internal and external multiple limit safety score of the three-phase unbalance sequence is low.

[0086] By comprehensively considering internal and external constraints, the security of the system can be evaluated more accurately and comprehensively, supporting the formulation of more comprehensive and reasonable distribution network decision configurations.

[0087] After calculating the internal and external multiple restriction safety scores of each sequence, the comprehensive internal and external multiple restriction safety score of the scheme is expressed as:

[0088]

[0089] In the formula, w δ 、w θ 、w σ , is the weight coefficient of each sequence, that is, the artificially preset weight values ​​of different sequences.

[0090] 2. Evaluation and simulation verification of distributed photovoltaic carrying capacity based on optimal safety, economic efficiency;

[0091] First, for the kth energy storage and transformer configuration scheme, the safety and economic efficiency of the distributed photovoltaic carrying capacity of the distribution network is calculated based on the comprehensive safety score of the scheme, the transformer expansion cost, and the open capacity of distributed photovoltaics, which is expressed as:

[0092]

[0093] Where η(k) is the safe and economic efficiency of the distributed photovoltaic carrying capacity of the distribution network of the kth scheme, C k is the transformer expansion cost of the kth solution, Q k is the open capacity of distributed photovoltaic power generation in the kth scheme, and Φ1, Φ2, and Φ3 are weight coefficients.

[0094] The optimal solution of the safety, economic efficiency and open capacity of distributed photovoltaic power generation in the distribution network is the final optimization solution, which is expressed as:

[0095]

[0096] In the formula, The final optimization solution.

[0097] Secondly, based on the final optimization scheme of energy storage and transformer configuration, the distributed photovoltaic carrying capacity assessment results, and the verification data set, the simulation system is used again to simulate and record the corresponding performance sequence. The current scheme comprehensive internal and external multiple restriction safety score Score is calculated by equations (9)-(14). sim .

[0098] If Score sim Does not meet the sequence internal and external restriction security score constraints, that is, Score sim ≥Score th , where Score th Limit the safety score threshold for the sequence inside and outside, or Score sim Compared with the previously calculated solution The scheme integrates multiple internal and external restrictions to score safety The gap is too large, Score thIf the safety score gap threshold is exceeded, or if the scheme is identified as a restricted scheme based on extreme or severe conditions in the performance sequence, the distributed PV carrying capacity assessment needs to be re-conducted.

Claims

1. A distributed photovoltaic carrying capacity assessment method, characterized in that: It includes the following two steps: (1) The safety scoring of the new energy carrying capacity of the distribution network based on multiple constraints inside and outside the sequence includes the following processes: Firstly, a distributed photovoltaic output and load power data set is constructed and divided into an optimization data set and a verification data set and stored in the database; Secondly, select the input parameter generation mode to input the time and energy storage configuration to be evaluated into the simulation system; Then, the simulation system uses the optimization data set to perform multi-layer iterative optimization, where the innermost layer optimizes energy storage charging and discharging, the middle layer optimizes transformer expansion, and the outermost layer optimizes energy storage configuration. It also records the transformer expansion cost, PV open capacity, and related performance sequences under different configurations. Finally, based on the fluctuation of the performance sequence and the internal and external restrictions of the sequence, it is determined whether to conduct a safety score with multiple restrictions inside and outside the sequence. If the performance sequence has extreme or multiple severe situations, or multiple performance sequences have non-ideal situations, the scheme is regarded as a restricted scheme and no score is given. For other schemes to be adopted, multiple restrictions inside and outside the sequence are considered, and a safety score with multiple restrictions inside and outside the sequence is conducted. Based on the relationship between the restriction scores inside different sequences, the adaptive weight coefficients of different performance sequences are adjusted according to the evaluation rules of the restriction scores outside the sequence. On the basis of calculating the safety scores of the internal and external restrictions of different performance sequences, the comprehensive safety score of the scheme with multiple restrictions inside and outside the sequence is finally obtained. (2) Evaluation and simulation verification of distributed photovoltaic carrying capacity based on optimal safety, economic efficiency, including the following processes: Firstly, based on the safety scores of multiple restrictions inside and outside the comprehensive sequence of the scheme, the cost of transformer expansion and the open capacity of distributed photovoltaic, the safety and economic efficiency of the distributed photovoltaic carrying capacity of the distribution network is calculated, and the scheme with the best combination of safety and economic efficiency and the open capacity of distributed photovoltaic is selected as the final optimization scheme. Secondly, based on the configuration scheme and the distributed PV carrying capacity assessment results, the simulation system uses the validation data set to perform simulations and record the corresponding performance sequences; Finally, a safety score is given to the final optimization scheme selected. If the difference between this score result and the previous score result of this scheme is greater than the threshold, or multiple restrictions occur, the carrying capacity needs to be re-evaluated.

2. A distributed photovoltaic carrying capacity assessment simulation system, characterized in that: Used to implement the evaluation method described in claim 1; including a database, a simulation scenario generation module, a simulation optimization module, a sequence internal and external multiple restriction safety score calculation module, a distributed photovoltaic carrying capacity safety and economic efficiency calculation module and a final optimization solution determination module; in: The database is used to store distributed photovoltaic output and load power data sets, including optimization and verification data sets; The simulation scenario generation module uses the stored data in the database and adopts two modes to generate distributed photovoltaic and load simulation scenarios; The simulation optimization module is used to simulate based on the input distributed photovoltaic and load simulation scenarios, using the optimization data set to obtain the optimal transformer capacity and distributed photovoltaic capacity through multi-layer iterative optimization, and record the corresponding performance sequence, or simulate based on the energy storage and transformer configuration scheme, distributed photovoltaic carrying capacity assessment results and verification data set, and record the corresponding performance sequence; The sequence internal and external multiple restriction safety score calculation module is used to perform sequence internal and external multiple restriction safety scores for different energy storage and transformer configuration schemes based on performance sequence fluctuations and sequence internal and external restrictions; The distributed photovoltaic carrying capacity safety and economic efficiency calculation module is used to calculate the safety and economic efficiency of the distributed photovoltaic carrying capacity of the distribution network based on the safety score, transformer expansion cost, and distributed photovoltaic open capacity, and assist in simulation optimization to obtain an optimization plan; The final optimization scheme determination module performs simulation verification based on the calculated sequence internal and external restriction safety scores, and outputs the evaluation results and the final optimization scheme; through this simulation system, the convenience and accuracy of distributed photovoltaic carrying capacity assessment are improved.

3. A distributed photovoltaic carrying capacity assessment simulation system according to claim 2, characterized in that: The simulation scene generation module uses two modes to generate simulation scenes, namely: The first mode is to select the distributed photovoltaic output data and load power data on day T from the database to generate scenarios; The second mode generates distributed photovoltaic and load power curves for day T according to the key parameters of the distributed photovoltaic output and load power probability distribution function, and performs scenario generation.

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