Cluster-power distribution network flexibility assessment method and system considering communication attack

Abstract

The invention discloses a cluster-power distribution network flexibility assessment method and system considering communication attacks, belongs to the technical field of power distribution network flexibility assessment, and establishes a cluster and power distribution network double-layer flexibility assessment architecture. On the cluster level, a false information injection attack modeling method based on robust optimization and a photovoltaic uncertainty modeling method based on distribution robust chance constraint are provided, and a cluster robust-random flexibility evaluation model is established. On the level of the power distribution network, a power distribution network random flexibility evaluation model is established and comprises a power distribution network flexibility range evaluation model based on a simplified DistFlow equation and a power distribution network baseline operation power and standby flexibility configuration model introducing a demand proportionality coefficient and a contribution coefficient. According to the method, the influence of communication attack and photovoltaic uncertainty on the flexibility of the power distribution network is considered, the upward and downward adjustment capability, the baseline operation power and the standby flexibility are further evaluated on the basis of the flexibility range, and comprehensive evaluation of the flexibility of the power distribution network is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network flexibility assessment, and specifically relates to a method and system for assessing the flexibility of a cluster-distribution network considering communication attacks. Background Art

[0002] The statements here only provide background art related to the present invention and do not necessarily constitute prior art.

[0003] How to explore and utilize the regulation potential of distributed resources in the distribution network to support the safe operation of the upper-level main network has been a much-concerned issue in recent years. Due to the large number and scattered layout of distributed resources, it is difficult for the upper-level distribution network to clarify the operation and control details of the underlying devices. Existing research mostly uses an aggregation method to construct a distributed resource cluster, and realizes the aggregation and assessment of flexibility through a hierarchical and partitioned architecture. However, in recent years, malicious attack events in the power industry have been frequent, threatening the information security of the power system. Compared with the dedicated communication network used in large power grids, the existing communication network on the user side where distributed resources are connected has disadvantages such as poor stability and lack of protection for open information nodes, and is more vulnerable to network attacks. In addition, there are many nodes in the distribution network with strongly random power injection, such as distributed photovoltaic power stations and rooftop photovoltaics scattered on the user side. These random disturbances superimposed on the possible malicious attack events make the problem of distribution network flexibility assessment very complex. In this context, the inventor found that the current flexibility assessment methods mainly have the following two problems: First, current research rarely considers the impact of communication attacks. However, communication attacks that maliciously tamper with the key parameter information of distributed resources will affect the accuracy of flexibility assessment. For example, if the state-of-charge data of a energy storage device that is originally close to the upper limit is tampered with and lowered during communication, then the upper limit of the original flexibility range will be wrongly expanded, and grid dispatchers will overestimate the regulation potential due to receiving incorrect data and formulate a scheduling control plan that is clearly unrealistic.

[0004] Second, in actual scheduling, the distribution network operator first needs to determine the baseline operating power as the normal operating power of the distribution network; secondly, it is necessary to ensure that the ratio of upward and downward regulation capabilities conforms to the power system requirements as much as possible; finally, it is necessary to reserve local standby flexibility to address the safety hazards brought by the uncertainty of photovoltaics, and clarify the contribution of each distributed resource cluster to the standby flexibility for subsequent decomposition and issuance of scheduling instructions. However, current research mostly focuses on the assessment of the flexibility range and does not further refine the flexibility, resulting in insufficient fine-grained flexibility assessment and difficulty in meeting actual scheduling requirements. Summary of the Invention

[0005] The object of the present invention is to overcome the deficiencies existing in the above-mentioned prior art, and to provide a method and system for evaluating the flexibility of a cluster-distribution network considering communication attacks, which takes into account the impacts of communication attacks and photovoltaic uncertainties on the flexibility of the distribution network, further evaluates the upward and downward regulation capabilities, baseline operating power, and reserve flexibility on the basis of the flexibility range, and realizes a comprehensive and integrated evaluation of the flexibility of the distribution network.

[0006] To achieve the above object, the present invention is implemented by the following technical solutions: On the one hand, the technical solution of the present invention provides a method for evaluating the flexibility of a cluster-distribution network considering communication attacks, including: Aggregating distributed resources into a cluster, extracting the initial state parameters of each distributed resource affected by communication, and establishing a compact form operating feasible region of distributed resources considering the influence of the communication process; Modeling false information injection attacks and establishing a robust box uncertainty set for the initial state parameters under the influence of communication attacks; Considering photovoltaic uncertainties, constructing a distributionally robust fuzzy set for the prediction deviation of photovoltaic power; Establishing a cluster robust-stochastic flexibility evaluation model, and solving to obtain the flexibility range reported to the distribution network, the reserve flexibility for local retention to cope with photovoltaic power uncertainties, and the reserve flexibility for local retention to cope with communication attacks; Establishing a distribution network flexibility range evaluation model, and solving to obtain the distribution network flexibility range under network constraints and the flexibility range that each cluster can be called; Establishing a distribution network baseline operating power and reserve flexibility configuration model, and solving to obtain the distribution network flexibility evaluation result.

[0007] In at least one embodiment, the extracting the initial state parameters of each distributed resource affected by communication and establishing a compact form operating feasible region of distributed resources considering the influence of the communication process specifically includes: Constructing the state equations of each distributed resource respectively; Separately extracting the initial state parameters affected by the communication process from the state equations as vectors, obtaining a compact representation of the distributed resource state equations, and establishing a compact form operating feasible region of distributed resources considering the influence of the communication process.

[0008] In at least one embodiment, the modeling false information injection attacks and establishing a robust box uncertainty set for the initial state parameters under the influence of communication attacks specifically includes: Modeling false information injection attacks by introducing false data parameters; Based on the false information injection attack model, obtaining the initial state parameters received by the cluster and the amplitude of the false information injection attack, and thus establishing a robust box uncertainty set for the initial state parameters under the influence of communication attacks.

[0009] In at least one embodiment, considering the photovoltaic uncertainty, a distributionally robust fuzzy set of photovoltaic power prediction deviation is constructed, which specifically includes: Considering the uncertainty of photovoltaic aggregated power, calculate the difference between the actual photovoltaic power and the predicted photovoltaic power in historical data, obtain a prediction deviation sample set containing multiple groups of data, and construct an empirical distribution of the prediction deviation; Based on the empirical distribution of the prediction deviation and the Wasserstein distance, establish a distributionally robust fuzzy set of photovoltaic power prediction deviation.

[0010] In at least one embodiment, the cluster robust-stochastic flexibility evaluation model is established, and the flexibility range reported to the distribution network, the local reserved flexibility for coping with photovoltaic power uncertainty, and the local reserved flexibility for coping with communication attacks are obtained by solving, which specifically includes: By adding spare flexibility chance constraints, establish a cluster robust-stochastic flexibility evaluation model considering photovoltaic power prediction deviation and communication attacks; For the compact form operation feasible region of distributed resources with uncertain parameters, based on robust duality transformation, transform it into a solvable form; Based on the distributionally robust optimization method, reconstruct the chance constraints into a probabilistic upper reserve constraint set and a probabilistic lower reserve constraint set; Transform the cluster robust-stochastic flexibility evaluation model into a convex optimization problem and solve it to obtain the upper and lower limits of the cluster flexibility range and the upper and lower spare flexibilities reserved locally for coping with photovoltaic power uncertainty; Calculate the differences between the upper limits and the lower limits of the two groups of flexibility ranges obtained when the robustness parameter takes 0 and the set value respectively, to obtain the upper and lower spare flexibilities reserved locally for coping with communication attacks.

[0011] In at least one embodiment, the distribution network flexibility range evaluation model is established, and the distribution network flexibility range under network constraints and the flexibility ranges that can be called by each cluster are obtained by solving, which specifically includes: Convert the cluster flexibility range into the flexibility range of the nodes where the clusters are located, and combine with the distribution network DistFlow power flow constraint set to establish a distribution network flexibility range evaluation model; Solve the distribution network flexibility range evaluation model to obtain the upper and lower limits of the distribution network flexibility range under network constraints, and use the values of the active power operation of the clusters in the two optimization results as the upper and lower limits of the flexibility ranges that can be called by each cluster.

[0012] In at least one embodiment, the distribution network flexibility evaluation results include the baseline operating power of the distribution network, the upward regulation capacity and downward regulation capacity reported to the dispatching center, the total reserve flexibility retained locally in the distribution network, the baseline operating power of each cluster, and the reserve flexibility borne by each cluster.

[0013] In at least one embodiment, to establish the baseline operating power and reserve flexibility configuration model of the distribution network and solve to obtain the distribution network flexibility evaluation results, specifically including: Construct an objective function, which includes: minimizing the reserve flexibility locally in the distribution network to cope with the power fluctuations of distributed photovoltaic power stations, and minimizing the gap between the ratio of the upward regulation capacity and downward regulation capacity in the evaluation results and the demand proportion coefficient; Construct constraint conditions, which include: integrating the power prediction deviations of photovoltaic power stations at each node and assigning the task of coping with the prediction deviations to each cluster through contribution coefficients, and ensuring that the probability that the reserve flexibility contributed by each cluster meets the prediction deviations is not less than the confidence parameter; Based on the objective function and constraint conditions, establish the baseline operating power and reserve flexibility configuration model of the distribution network and solve it to obtain the total reserve flexibility of the distribution network to cope with the power deviation of the photovoltaic power station, as well as the reserve flexibility and contribution factors shared by each cluster.

[0014] In at least one embodiment, take the value of the injected power at the root node in the optimization result of the baseline operating power and reserve flexibility configuration model of the distribution network as the baseline operating power of the distribution network; take the value of the cluster operating power in the optimization result of the baseline operating power and reserve flexibility configuration model of the distribution network as the baseline operating power of each cluster.

[0015] On the other hand, the technical solution of the present invention also provides a cluster-distribution network flexibility evaluation system considering communication attacks, including: The first establishment module is configured to: aggregate distributed resources into clusters, extract the initial state parameters of each distributed resource affected by communication, and establish a compact form operating feasible region of distributed resources considering the influence of the communication process; The second establishment module is configured to: model false information injection attacks and establish a robust box-type uncertainty set of the initial state parameters under the influence of communication attacks; The third establishment module is configured to: consider photovoltaic uncertainty and construct a distributionally robust fuzzy set of photovoltaic power prediction deviations; The cluster evaluation module is configured to: establish a cluster robust-stochastic flexibility evaluation model and solve to obtain the flexibility range reported to the distribution network, the reserve flexibility retained locally to cope with photovoltaic power uncertainty, and the reserve flexibility retained locally to cope with communication attacks; The first distribution network evaluation and establishment module is configured to: establish a distribution network flexibility range evaluation model, and solve to obtain the distribution network flexibility range under network constraints and the flexibility range that can be called by each cluster; The second distribution network evaluation module is configured to: establish a distribution network baseline operating power and reserve flexibility configuration model, and solve to obtain the distribution network flexibility evaluation result.

[0016] The beneficial effects of the above technical solutions of the present invention are as follows: The cluster-distribution network flexibility evaluation method considering communication attacks of the present invention aggregates decentralized distributed resources into clusters and establishes a two-layer flexibility evaluation architecture for clusters and distribution networks. At the cluster level, a false information injection attack modeling method based on robust optimization and a photovoltaic uncertainty modeling method based on distributionally robust chance constraints are proposed, and a cluster robust-stochastic flexibility evaluation model is established. At the distribution network level, a distribution network stochastic flexibility evaluation model is established, including a distribution network flexibility range evaluation model based on the simplified DistFlow equation and a distribution network baseline operating power and reserve flexibility configuration model introducing demand ratio coefficients and contribution coefficients. The present invention considers the impacts of communication attacks and photovoltaic uncertainties on the flexibility of the distribution network, and further evaluates the upward and downward regulation capabilities, baseline operating power, and reserve flexibility on the basis of the flexibility range, realizing a comprehensive and integrated evaluation of the flexibility of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The specification drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0018] Figure 1 It is a schematic flowchart of the steps of the cluster-distribution network flexibility evaluation method considering communication attacks proposed in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further descriptions of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0020] As introduced in the background art, the purpose of the present invention is to overcome the above-mentioned deficiencies existing in the prior art, and provide a cluster-distribution network flexibility evaluation method and system considering communication attacks, which considers the impacts of communication attacks and photovoltaic uncertainties on the flexibility of the distribution network, and further evaluates the upward and downward regulation capabilities, baseline operating power, and reserve flexibility on the basis of the flexibility range, realizing a comprehensive and integrated evaluation of the flexibility of the distribution network.

[0021] Embodiment 1 In a typical embodiment of the present invention, as Figure 1 shown, this embodiment discloses a method for evaluating the flexibility of a cluster-distribution network considering communication attacks, including the following steps: S1. Aggregate distributed resources into a cluster, extract the initial state parameters of each distributed resource affected by communication, and establish a compact form of the operating feasible region of the distributed resources considering the influence of the communication process; S2. Model false information injection attacks and establish a robust box uncertainty set for the initial state parameters under the influence of communication attacks; S3. Consider the uncertainty of photovoltaic power, and construct a distributionally robust fuzzy set for the prediction deviation of photovoltaic power; S4. Establish a cluster robust-stochastic flexibility evaluation model, and solve to obtain the flexibility range reported to the distribution network, the local reserved flexibility to cope with the uncertainty of photovoltaic power, and the local reserved flexibility to cope with communication attacks; S5. Establish a distribution network flexibility range evaluation model, and solve to obtain the flexibility range of the distribution network under network constraints and the flexibility range that can be called by each cluster; S6. Establish a distribution network baseline operating power and reserve flexibility configuration model, and solve to obtain the distribution network flexibility evaluation result.

[0022] The above method for evaluating the flexibility of a cluster-distribution network considering communication attacks will be described in detail below in combination with specific embodiments.

[0023] S1. Aggregate dispersed distributed resources into a cluster, establish a two-layer flexibility evaluation architecture for the cluster-distribution network, focus the research object on three types of distributed resources, namely temperature-controlled loads, distributed energy storage, and electric vehicles, respectively construct the state equations of the distributed resources, and separately extract the initial state parameters affected by communication in the state equations, and establish a compact form of the operating feasible region of the distributed resources considering the influence of the communication process. The specific process is as follows: S101. Respectively construct the state equations of temperature-controlled loads, distributed energy storage, and electric vehicles.

[0024] In this step, based on the equivalent thermal parameter model, the state equation of the temperature-controlled load is constructed, which is specifically expressed as: (1); In the formula, respectively represent the total number and width of the evaluation time period, represents the set of integers; represents t the operating power of the n th temperature-controlled load in the time period; t represents n the indoor temperature of the represents the initial indoor temperature of the n th temperature-controlled load; represents t the outdoor temperature during a time period; represents an auxiliary parameter; respectively represent the n thermal resistance, heat capacity, and refrigeration / heating performance coefficient of the th temperature-controlled load; n respectively represent the upper and lower limits of the indoor temperature of the

[0025] Based on the charge and discharge operation model, a distributed energy storage state equation is constructed, specifically expressed as: (2); In the formula: respectively represent t the charging power and discharging power of the n th energy storage device during a time period; represents t the state of charge of the n th energy storage device during a time period; represents the n initial state of charge of the th energy storage device; n represents the self-discharge coefficient of the n th energy storage device; respectively represent the n upper and lower limits of the capacity of the

[0026] Based on the charge and discharge operation model, a grid-connected electric vehicle state equation is constructed, specifically expressed as: (3); In the formula: respectively represent t the charging and discharging powers of the n th electric vehicle during a time period; represents t the state of charge of the n th electric vehicle during a time period; represents the n initial state of charge of the th electric vehicle; n represents the charge and discharge efficiency coefficient of the t th electric vehicle; n respectively represent the upper and lower limits of the state of charge of the

[0027] S102. Before each evaluation starts, the initial state parameters of each distributed resource need to be measured in real time by the user-side intelligent terminal and transmitted to the cluster through the user-side communication network. The initial state parameters affected by the communication process are separately extracted from the state equation as a vector, so as to obtain a compact representation of the distributed resource state equation, which is specifically expressed as: (4); In the formula: represents the auxiliary matrix; represents the decision vector; represents the initial state vector affected by communication; respectively represent the upper limit vector and the lower limit vector of the distributed resource state parameters.

[0028] For the thermostatic load, is abbreviated as , then the auxiliary matrix in formula (4) and each vector are expressed as: (5); For distributed energy storage, is abbreviated as , then the auxiliary matrix in formula (4) and each vector are expressed as: (6); For grid-connected electric vehicles, is abbreviated as , then the auxiliary matrix in formula (4) and each vector are expressed as: (7); S103. Based on the compact representation of the distributed resource state equation in S102, a compact form operating feasible region of the distributed resource considering the influence of the communication process is established, which is specifically expressed as: (8); In the formula: represents the operating feasible region; represents the operating power constraint set of each distributed resource; represents the operating power of each distributed resource.

[0029] S2. Model the False Data Injection (FDI) attack and establish a robust box uncertainty set for the initial state parameters under the influence of communication attacks.

[0030] S201. Model the false data injection attack by introducing false data parameters, which is specifically expressed as: (9); Where: represents the false data parameter, which is 0 when no false information injection attack occurs; represents the initial state parameter received by the cluster; represents the true initial state parameter; represents the amplitude of the false information injection attack.

[0031] S202. Obtain the initial state parameter received by the cluster and the amplitude of the false information injection attack from the false information injection attack model constructed based on S201, and establish a robust box uncertainty set of the initial state parameter under the influence of communication attack, which is specifically expressed as: (10); Where: represents the robust box uncertainty set; respectively represent the upper and lower limits of the uncertainty of the initial state parameter.

[0032] S3. Considering the photovoltaic uncertainty, construct a distributionally robust fuzzy set of the photovoltaic power prediction deviation.

[0033] S301. Considering that the aggregated power of rooftop photovoltaics in the cluster is uncertain, therefore, based on the uncertainty of the aggregated photovoltaic power, the aggregated photovoltaic power is expressed as: (11); Where: represents the actual value of the aggregated photovoltaic power; represents the predicted value, represents the deviation between the predicted value and the actual value.

[0034] S302. By calculating the difference between the actual photovoltaic power and the predicted photovoltaic power in the historical data, obtain a prediction deviation sample set containing M groups of data , where represents the m - th prediction deviation vector. Based on the prediction sample deviation set, construct an empirical distribution of the prediction deviation, which is specifically expressed as: (12); Where: represents the empirical distribution; represents 's Dirac measure.

[0035] S303. Based on the empirical distribution of the prediction deviation and the Wasserstein distance, establish a distributionally robust fuzzy set of the photovoltaic power prediction deviation, which is specifically expressed as: (13); In the formula: represents the distributionally robust fuzzy set; represents the probability distribution of the prediction deviation; represents the Wasserstein distance; represents the Wasserstein ball radius; represents the set of all distributions that satisfy the polyhedral support set; represents the polyhedral support set, where represents the deviation vector, respectively represent the support matrix and the support vector.

[0036] S4. Establish a cluster robust-stochastic flexibility evaluation model, and solve to obtain the flexibility range of the reported distribution network, the local reserved reserve flexibility for coping with photovoltaic power uncertainty, and the local reserved reserve flexibility for coping with communication attacks.

[0037] S401. By adding reserve flexibility opportunity constraints, establish a cluster robust-stochastic flexibility evaluation model considering photovoltaic power prediction deviation and communication attacks, which is specifically expressed as: (14); (15); In the formula: respectively represent the upper and lower limits of the cluster flexibility range; N represents the total number of distributed resources participating in the evaluation within the cluster; represents the resource set; represents the aggregated node power at time t; represents the confidence parameter; respectively represent the upper reserve flexibility and the lower reserve flexibility reserved locally for coping with random injection; represents the other fixed load power within the node aggregator.

[0038] S402. For the compact form operation feasible region of distributed resources with uncertain parameters, based on robust duality transformation, transform it into a solvable form, which is specifically expressed as: (16); In the formula: represents the robust operation feasible region of distributed resources; represents the initial state parameter vector received by the cluster; represents the amplitude vector of the false information injection attack; and represent the auxiliary decision vector, represents the set of real numbers; represents the auxiliary decision variable; represents the robustness parameter, and the higher the value, the higher the conservatism.

[0039] S403. Based on the distributionally robust optimization method, the chance constraint is reconstructed into a probabilistic upper reserve constraint set and a probabilistic lower reserve constraint set.

[0040] Taking the upper reserve flexibility as an example, the probabilistic upper reserve constraint set is specifically expressed as: (17); In the formula: denotes the probabilistic upper reserve constraint set; and a denotes the distributionally robust decision vector; denotes the distributionally robust decision matrix, denotes the element in the decision matrix; denotes the auxiliary vector, where except the t-th element is 1, the rest of the elements are all 0.

[0041] Similarly, the probabilistic lower reserve constraint set is calculated.

[0042] S404. The cluster robust-stochastic flexibility evaluation model is transformed into a convex optimization problem, which is specifically expressed as: (18); (19); Solving the convex optimization problem can obtain the upper limit and the lower limit of the cluster flexibility range, as well as the upper and lower reserve flexibilities and the lower reserve flexibility for local reservation to cope with the uncertainty of photovoltaic power. is used in the next evaluation process.

[0043] The upper and lower reserve flexibilities for local reservation to cope with communication attacks are implicit. Taking the upper reserve flexibility as an example, it can be obtained by calculating the difference between the two sets of upper limits of the flexibility range when the robustness parameter takes 0 (indicating ignoring communication attacks) and the set value : (20); In the formula: respectively denote the upper limits of the flexibility obtained when the robustness parameter takes 0 and the set value . Similarly, the lower reserve flexibility for local reservation to cope with communication attacks is obtained by calculating the difference between the two sets of lower limits of the flexibility range when the robustness parameter takes 0 (indicating ignoring communication attacks) and the set value .

[0044] S5. Establish a distribution network flexibility range evaluation model, solve to obtain the distribution network flexibility range under network constraints and the flexibility range that can be called by each cluster, and use this as the first part of the distribution network stochastic flexibility evaluation model. The specific process is as follows: S501. Based on the simplified DistFlow model, establish the distribution network power flow constraint set, which is specifically expressed as: (21); In the formula: represents the distribution network DistFlow power flow constraint set; represents t the active and reactive power flowing from node i to node j during the period; t represents the fixed active and reactive load power of node j during the period; t represents the active and reactive operating power of cluster j during the period, and the power factor is taken as 0.85; t represents the predicted active power of the distributed photovoltaic power station at node j during the period, and the photovoltaic reactive power is adjusted to 0; ij respectively represent the resistance and reactance of branch ; ij respectively represent the lower active limit, upper active limit, lower reactive limit and upper reactive limit of branch ; t represents the square of the voltage of node j during the period;

[0045] S502. Based on the upper limit and lower limit of the cluster flexibility range obtained in S404, after reporting to the distribution network operator, they are converted into the upper limit and lower limit of the flexibility range of the node where the cluster is located. On this basis, combine the distribution network DistFlow power flow constraint set obtained in S501 to establish a distribution network flexibility range evaluation model, which is specifically expressed as: (22); (23); In the formula: respectively represent the upper limit and lower limit of the distribution network flexibility range under network constraints; represents t the injection power at the root node during the Indicate t Time period cluster j Upper and lower limits of the flexibility range for uploading; Indicate the set of grid-connected nodes of the cluster.

[0046] The upper limit of the flexibility range of the distribution network under network constraints is obtained by solving the above-mentioned flexibility range evaluation model of the distribution network And the lower limit ,And take the active operating power of the cluster in the two optimization results The value of is used as the upper limit of the flexibility range that each cluster can be called And the lower limit 。Among them, the first optimization result is the optimal value corresponding to each decision variable obtained by solving model (22), and the second optimization result is the optimal value corresponding to each decision variable obtained by solving model (23).

[0047] S6. As the second part of the distribution network stochastic flexibility evaluation model, establish a distribution network baseline operating power and reserve flexibility configuration model, and solve to obtain the distribution network flexibility evaluation results, including the distribution network baseline operating power, the upward regulation ability and downward regulation ability reported to the dispatching center, the total reserve flexibility retained in the distribution network locally, the baseline operating power of each cluster and the reserve flexibility borne, and the specific process is as follows: S601. Construct the objective function. The optimization objectives of the distribution network operator include two parts. The first part is to minimize the reserve flexibility used locally in the distribution network to cope with the power fluctuations of distributed photovoltaic power stations, so as to maximize the flexibility reported to the dispatching center while meeting the local reserve demand. The second part is to minimize the gap between the ratio of the upward regulation ability and downward regulation ability in the evaluation results and the demand ratio coefficient, so that the evaluation results can meet the needs of the dispatching center to the greatest extent. The mathematical form of the objective function is as follows: (24); In the formula: respectively represent the upper reserve flexibility and lower reserve flexibility contributed by the jth cluster in the tth time period; respectively represent the upward regulation ability vector and downward regulation ability vector reported to the dispatching center in the tth time period; represents the flexibility demand ratio coefficient issued by the dispatching center.

[0048] S602. Construct the constraint conditions. On the one hand, the distribution network operator needs to integrate the power prediction deviations of each node's photovoltaic power station and allocate the task of coping with the prediction deviations to each cluster through the contribution coefficient; on the other hand, it needs to ensure through chance constraints that the probability that the reserve flexibility contributed by each cluster meets the prediction deviation is not less than the confidence parameter 。The mathematical form of the constraint conditions is as follows: (25); In the formula: respectively represent the upward regulation capacity and downward regulation capacity reported by the distribution network operator to the dispatching center in the t-th period; respectively represent the upper reserve flexibility and lower reserve flexibility retained locally by the distribution network operator in the t-th period; represents the set of grid-connected nodes of distributed photovoltaic power stations; respectively represent the upper and lower limits of the flexibility range of the distribution network under network constraints obtained in step five; respectively represent the upper and lower limits of the flexibility range that can be called for each cluster obtained in step five; represents the power prediction deviation of the j-th distributed photovoltaic power station in the t-th period; represents the total prediction deviation; represents the standby contribution coefficient of the j-th cluster in the t-th period.

[0049] S603. Establish and solve a distribution network baseline operating power and reserve flexibility configuration model based on the above two objective functions and two constraint conditions.

[0050] Specifically, in this step, the complex chance constraint represented by formula (25) can be transformed into a convex constraint form similar to formula (17), and then the distribution network baseline operating power and reserve flexibility configuration model is transformed into a convex optimization problem that can be directly solved. By solving the model, the total reserve flexibility of the distribution network to cope with the power deviation of the photovoltaic power station is obtained , as well as the reserve flexibility and contribution factors shared by each cluster . Further, the distribution network baseline operating power and the baseline operating power of each cluster are the values of the injected power at the root node and the cluster operating power in the optimization results of the distribution network baseline operating power and reserve flexibility configuration model.

[0051] Embodiment 2 In a typical implementation manner of the present invention, this embodiment discloses a cluster-distribution network flexibility evaluation system considering communication attacks, including: The first establishment module is configured to: aggregate distributed resources into clusters, extract the initial state parameters of each distributed resource affected by communication, and establish a compact form operating feasible region of distributed resources considering the influence of the communication process; The second establishment module is configured to: model false information injection attacks and establish a robust box uncertainty set of the initial state parameters under the influence of communication attacks; The third establishment module is configured to: consider photovoltaic uncertainty and construct a distributionally robust fuzzy set of photovoltaic power prediction deviations; The cluster evaluation module is configured to: establish a cluster robust-stochastic flexibility evaluation model and solve to obtain the cluster flexibility range; The first distribution network evaluation establishment module is configured to: establish a distribution network flexibility range evaluation model and solve to obtain the distribution network flexibility range under network constraints and the flexibility range that can be called by each cluster; The second distribution network evaluation module is configured to: establish a distribution network baseline operating power and reserve flexibility configuration model and solve to obtain the distribution network flexibility evaluation result The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating the flexibility of a cluster-distribution network considering communication attacks, characterized in that, Including: Aggregating distributed resources into a cluster, extracting the initial state parameters of each distributed resource affected by communication, and establishing a compact-form operation feasible region of distributed resources considering the influence of the communication process; Modeling false information injection attacks and establishing a robust box uncertainty set for the initial state parameters under communication attacks; Considering the uncertainty of photovoltaic power, constructing a distributionally robust fuzzy set for the prediction deviation of photovoltaic power; Establishing a cluster robust-stochastic flexibility evaluation model, and solving to obtain the flexibility range reported to the distribution network, the reserved local flexibility for coping with photovoltaic power uncertainty, and the reserved local flexibility for coping with communication attacks; Establishing a distribution network flexibility range evaluation model, and solving to obtain the distribution network flexibility range under network constraints and the flexibility range that can be called by each cluster; Establishing a distribution network baseline operating power and reserved flexibility configuration model, and solving to obtain the distribution network flexibility evaluation result.

2. The cluster-distribution network flexibility evaluation method considering communication attacks according to claim 1, wherein The step of extracting the initial state parameters of each distributed resource affected by communication and establishing a compact-form operation feasible region of distributed resources considering the influence of the communication process specifically includes: Constructing the state equations of each distributed resource respectively; Separately extracting the initial state parameters affected by the communication process from the state equations as vectors, obtaining a compact representation of the distributed resource state equations, and establishing a compact-form operation feasible region of distributed resources considering the influence of the communication process.

3. The method for evaluating the flexibility of a cluster-distribution network considering communication attacks according to claim 1, characterized in that, The step of modeling false information injection attacks and establishing a robust box uncertainty set for the initial state parameters under communication attacks specifically includes: Modeling false information injection attacks by introducing false data parameters; Based on the false information injection attack model, obtaining the initial state parameters received by the cluster and the amplitude of the false information injection attack, and thus establishing a robust box uncertainty set for the initial state parameters under communication attacks.

4. The cluster-distribution network flexibility evaluation method considering communication attacks according to claim 1, characterized in that The step of considering the uncertainty of photovoltaic power and constructing a distributionally robust fuzzy set for the prediction deviation of photovoltaic power specifically includes: Considering the uncertainty of the aggregated photovoltaic power, calculating the difference between the actual photovoltaic power and the predicted photovoltaic power in historical data to obtain a prediction deviation sample set containing multiple groups of data, and constructing an empirical distribution of the prediction deviation; Based on the empirical distribution of the prediction deviation and the Wasserstein distance, establishing a distributionally robust fuzzy set for the prediction deviation of photovoltaic power.

5. The method for evaluating the flexibility of a cluster-distribution network considering communication attacks according to claim 1, wherein, The step of establishing a cluster robust-stochastic flexibility evaluation model and solving to obtain the flexibility range reported to the distribution network, the reserved local flexibility for coping with photovoltaic power uncertainty, and the reserved local flexibility for coping with communication attacks specifically includes: By adding reserved flexibility chance constraints, establishing a cluster robust-stochastic flexibility evaluation model considering the prediction deviation of photovoltaic power and communication attacks; For the compact-form operation feasible region of distributed resources with uncertain parameters, transforming it into a solvable form based on robust duality transformation; Based on the distributionally robust optimization method, reconstructing the chance constraints into a probabilistic upper reserve constraint set and a probabilistic lower reserve constraint set; Transforming the cluster robust-stochastic flexibility evaluation model into a convex optimization problem and solving it to obtain the upper and lower limits of the cluster flexibility range and the upper and lower reserved flexibilities for coping with photovoltaic power uncertainty locally reserved. Calculate the difference between the upper limits and the difference between the lower limits of the two sets of flexibility ranges obtained when the robustness parameter takes 0 and the set value respectively, to obtain the upper reserve flexibility and the lower reserve flexibility for the local area to cope with communication attacks.

6. The method for evaluating the flexibility of the cluster-distribution network considering communication attacks according to claim 1, wherein The establishment of the distribution network flexibility range evaluation model, and solving to obtain the distribution network flexibility range under network constraints and the flexibility ranges that can be called by each cluster, specifically includes: Convert the flexibility range of the cluster into the flexibility range of the node where the cluster is located, and establish a distribution network flexibility range evaluation model in combination with the distribution network DistFlow power flow constraint set; Solve the distribution network flexibility range evaluation model to obtain the upper and lower limits of the distribution network flexibility range under network constraints, and use the values of the active operating power of the clusters in the two optimization results as the upper and lower limits of the flexibility ranges that can be called by each cluster.

7. The method for evaluating the flexibility of a cluster-distribution network considering communication attacks according to claim 1, wherein, The distribution network flexibility evaluation results include the distribution network baseline operating power, the upward regulation capacity and the downward regulation capacity reported to the dispatching center, the total reserve flexibility retained locally in the distribution network, the baseline operating power of each cluster and the reserve flexibility borne.

8. The method for evaluating the flexibility of a cluster-distribution network considering communication attacks according to claim 1, wherein The establishment of the distribution network baseline operating power and reserve flexibility configuration model, and solving to obtain the distribution network flexibility evaluation results, specifically includes: Construct an objective function, and the objective function includes: minimizing the reserve flexibility of the distribution network locally to cope with the power fluctuations of distributed photovoltaic power stations and minimizing the gap between the ratio of the upward regulation capacity and the downward regulation capacity in the evaluation results and the demand proportionality coefficient; Construct constraint conditions, and the constraint conditions include: integrating the power prediction deviations of photovoltaic power stations at each node and assigning the task of coping with the prediction deviations to each cluster through contribution coefficients and ensuring that the probability that the reserve flexibility contributed by each cluster meets the prediction deviation is not less than the confidence parameter; Based on the objective function and the constraint conditions, establish and solve the distribution network baseline operating power and reserve flexibility configuration model to obtain the total reserve flexibility of the distribution network to cope with the power deviation of the photovoltaic power station, as well as the reserve flexibility and contribution factors shared by each cluster.

9. The method for evaluating the flexibility of the cluster-distribution network considering communication attacks according to claim 8, wherein Take the value of the injected power at the root node in the optimization result of the distribution network baseline operating power and reserve flexibility configuration model as the distribution network baseline operating power; take the value of the cluster operating power in the optimization result of the distribution network baseline operating power and reserve flexibility configuration model as the baseline operating power of each cluster.

10. A cluster-distribution network flexibility evaluation system considering communication attacks, characterized in that, Including: The first establishment module is configured to: aggregate distributed resources into clusters, extract the initial state parameters of each distributed resource affected by communication, and establish a compact form operating feasible region of distributed resources considering the influence of the communication process; The second establishment module is configured to: model false information injection attacks and establish a robust box uncertainty set of the initial state parameters under the influence of communication attacks; The third establishment module is configured to: consider photovoltaic uncertainty and construct a distributionally robust fuzzy set of photovoltaic power prediction deviations; The cluster evaluation module is configured to: solve to obtain the flexibility range reported to the distribution network, the reserve flexibility locally retained to cope with photovoltaic power uncertainty, and the reserve flexibility locally retained to cope with communication attacks; The first distribution network evaluation and establishment module is configured to: establish a distribution network flexibility range evaluation model, and solve to obtain the distribution network flexibility range under network constraints and the flexibility range that can be called by each cluster; The second distribution network evaluation module is configured to: establish a distribution network baseline operating power and reserve flexibility configuration model, and solve to obtain the distribution network flexibility evaluation result.

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