Intelligent power network toughness enhancement energy configuration method and system oriented to changeable environment

Abstract

The invention provides an intelligent power network toughness enhancement energy configuration method and system oriented to a changeable environment. The method comprises the following steps: generating a typical scene according to the uncertainty of wind power and photovoltaic power generation output power; constructing a power line fault model according to an N-K criterion to simulate an extreme situation of simultaneous faults of a plurality of lines; considering each typical scene and fault situation, and establishing an optimization model with the purpose of minimizing the line loss; building a two-stage solving structure of a master problem and a slave problem based on the optimization model; a decision consistency constraint is set between a master problem and a slave problem, and relaxation and updating are carried out based on the constraint; and iterative solution is carried out until a global optimal energy configuration scheme is obtained. According to the invention, flexible response to short-term extreme faults can be realized while long-term cost effectiveness is ensured, so that the overall toughness and reliability of a power network are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power networks, and specifically relates to a method and system for energy configuration with enhanced resilience in intelligent power networks for changing environments. Background Art

[0002] In modern power systems, the power distribution network, as a critical link between power generation and end users, is often the first to be affected and experience failures when subjected to extreme environmental shocks such as strong winds, heavy rain, freezing temperatures, geological disasters, or cyberattacks. With the increasing threat of extreme weather conditions and potential attacks worldwide, the vulnerability of distribution networks is becoming increasingly apparent. Large-scale line failures not only disrupt power supply to some users but, if not restored in a timely manner, can also trigger a chain reaction and expand the scope of power outages. Against this backdrop, improving the adaptability and resilience of power grids in the face of changing environments has become a key issue that needs to be addressed in power system planning and operation.

[0003] Existing technologies for studying the resilience of power distribution networks primarily focus on distributed energy configuration, aiming to improve the network's resistance to external shocks and self-healing capabilities by rationally deploying renewable energy units and energy storage devices within the network. However, in practice, there are two common configuration methods: Large-scale fixed unit deployment from a long-term planning perspective: This approach provides basic and stable energy support for the grid by planning large-capacity renewable energy generation equipment or fixed energy storage systems. However, this approach focuses primarily on long-term average demand and lacks adaptability to short-term emergencies, making it difficult to respond promptly to rapidly changing loads and fault conditions.

[0004] Small-scale flexible equipment deployment from a short-term scheduling perspective: In the event of a failure or emergency, flexible resources such as mobile energy storage and emergency power supplies are added to meet instantaneous demand fluctuations. While this approach can respond quickly, its scale or capacity is often small. Without integration with long-term planning, it is difficult to balance overall economic efficiency and long-term reliability, resulting in wasted resources or an inability to cope with cumulative demand.

[0005] When faced with two major uncertainties: renewable energy output fluctuations and natural disasters, traditional methods also have the following shortcomings: Context-aware optimization: Scenario enumeration or scenario sets are often used to describe the random fluctuations of renewable energy sources such as wind power and photovoltaics. While multiple scenarios can be evaluated in long-term planning, the computational complexity increases significantly when the number of scenarios is large, and inadequate scenario coverage can lead to results that deviate from actual operational requirements.

[0006] Emergency dispatch optimization: By constructing extreme fault scenarios (such as simultaneous faults on multiple lines), the system's certain defense capabilities are ensured, but there are often defects such as being overly conservative or only designed for the worst-case scenario, resulting in redundant investment and insufficient flexibility.

[0007] Therefore, the existing configuration strategies have limitations in balancing long-term stability and short-term emergency flexibility, and it is difficult to fully improve the resilience of the distribution network in a changing environment. Summary of the Invention

[0008] The purpose of the present invention is to solve the deficiencies existing in the above-mentioned background technology, and provide an intelligent power network resilience-enhancing energy configuration method and system for a changing environment. By combining scenario-driven optimization and emergency response scheduling, the configuration of fixed energy units and the deployment of flexible energy units are incorporated into a unified multi-stage optimization framework. By setting decision consistency constraints between the master problem and the slave problems and using a collaborative solution algorithm, it is possible to flexibly respond to short-term extreme faults while ensuring long-term cost-effectiveness, thereby significantly improving the overall resilience and reliability of the power network.

[0009] The technical solution adopted by the present invention is: An intelligent power network resilience-enhancing energy configuration method for a changing environment, including the following steps: Generate typical scenarios based on the uncertainty of the output power of wind power and photovoltaic power generation; construct a power line fault model according to the N-K criterion to simulate extreme situations of simultaneous faults on multiple lines; Considering each typical scenario and fault situation, establish an optimization model with the goal of minimizing line losses; set binary decision variables to represent the configuration status of distributed energy units and the topological reconstruction of the power grid lines; Based on the optimization model, build a two-level solution structure for the master problem and the slave problems. Take the configuration of fixed energy units and network topology decision-making as the master problem, and take scenario-driven optimization and emergency response scheduling as the two slave problems respectively; among them, scenario-driven optimization is used for long-term planning to determine the optimal configuration of fixed energy units, and emergency response scheduling is used for short-term scheduling to determine the optimal deployment of flexible energy units; Set decision consistency constraints between the master problem and the slave problems, and perform relaxation and update based on this constraint; iteratively solve until a global optimal energy configuration scheme is obtained, thereby improving the resilience of the power network in an uncertain environment.

[0010] In the above technical solution, the modeling of the output uncertainty of wind power and photovoltaic power generation adopts a scenario generation method that combines Latin hypercube sampling and scenario reduction to obtain a number of representative wind speed and irradiance scenarios and their occurrence probabilities. Among them, a large number of initial scenarios are generated from the probability distributions of wind speed and solar irradiance through Latin hypercube sampling, and then the initial scenario set is compressed into a set of representative typical scenarios by using a scenario reduction algorithm.

[0011] In the above technical solution, the line fault model is established by introducing binary variables representing the attacked state of power lines, and a constraint condition that the number of attacked lines does not exceed K is set to characterize the situation where at most K power lines fail simultaneously in a variable environment. When the binary variable of a certain power line indicates that the line is attacked, the line is regarded as failing and disconnected to limit the power flow distribution in the corresponding fault scenario in the optimization model.

[0012] In the above technical solution, binary decision variables are set in the energy configuration optimization model to depict the distributed energy configuration and power grid topology reconstruction decisions: The first type of binary variable is used to indicate whether a fixed energy unit or a flexible energy storage device is installed at each power node. If it is installed at the corresponding node, the value is 1, otherwise the value is 0. The second type of binary variable is used to indicate the operating state of the power line. If the line is put into operation, the value is 1, and if the line is disconnected, the value is 0. In this way, the installation selection of distributed energy units and the power grid reconstruction plan are introduced into the optimization decision.

[0013] In the above technical solution, the energy configuration optimization model is a two-stage optimization model. The first stage configures fixed energy units, and the second stage configures flexible energy units and power grid reconstruction constraints. Its objective function includes the configuration cost of fixed energy units, the power grid line loss cost, the configuration cost of flexible energy units, and the load loss cost in extreme fault scenarios. The constraint conditions of the optimization model include the power flow balance constraint of the power network, the power output limit of generators and distributed energy units, the line transmission capacity limit, the upper and lower limits of node voltage constraints, the configuration quantity constraint of fixed energy units, the configuration quantity constraint of flexible energy units, and the constraint that the number of damaged lines does not exceed K under the line fault model.

[0014] In the above technical solution, the main problem aims to minimize the configuration cost of stable energy units, and the constraints include the total installation quantity limit of fixed energy units at all nodes, the total configurable quantity limit of wind power devices, and the total installation quantity limit of photovoltaic power generation devices at each node combined.

[0015] In the above technical solution, the sub-problem of scenario-driven optimization aims to minimize the line loss weighted by the occurrence probabilities of various wind power and photovoltaic scenarios. The constraints include the power flow equations and balance conditions of the power network under normal operating conditions, as well as the operation and capacity limitations of the fixed energy units. The decomposed probabilistic optimization algorithm is used for solution, so as to determine the optimal configuration scheme of the fixed energy units at each power node.

[0016] In the above technical solution, the sub-problem of emergency response scheduling aims to minimize the load loss caused by the failure of the attacked line. The constraints include the power flow balance constraint of the power network under the fault scenario, the operation constraint of the flexible energy units, and the power grid topology reconstruction constraint. The deployment scheme of the flexible energy units is iteratively optimized through the incremental collaborative solution algorithm.

[0017] In the above technical solution, a coupling mechanism for decision consistency is established between the master problem and the sub-problems, and the master problem and the sub-problems are solved collaboratively by using the method of iterative updating of the Lagrange multipliers; by adding the coupling constraints to the Lagrangian relaxation and introducing the corresponding Lagrange multipliers, a Lagrangian relaxation model including the master problem and the sub-problems is constructed, and then the master problem and the sub-problems are alternately solved and the Lagrange multipliers are updated according to the deviation of the solutions after each iteration. Such a cycle is repeated until convergence, so as to realize the incremental collaborative optimization among the sub-models and obtain the global optimal solution that satisfies all the constraints.

[0018] The present invention also provides an intelligent power network resilience-enhanced energy configuration system for a variable environment, which is used to implement the method described in the above technical solution, and includes: An uncertainty modeling module, which is used to perform uncertainty analysis on the outputs of wind power and photovoltaic power generation and generate a corresponding set of typical wind speed and irradiance scenarios; A fault scenario generation module, which is used to construct a power network line fault scenario based on the N-K criterion and determine the combination and quantity limit of the attacked lines under the fault scenario; An optimization model construction module, which is used to establish a distributed energy configuration optimization model with the minimum grid line loss as the goal, and set binary decision variables representing the installation status of distributed energy units and the grid line reconstruction status in the model; A model decomposition module, which builds a two-level solution structure of the master problem and the sub-problems based on the optimization model, takes the configuration of fixed energy units and network topology decision as the master problem, and takes scenario-driven optimization and emergency response scheduling as two sub-problems respectively; among them, scenario-driven optimization is used for long-term planning to determine the optimal configuration of fixed energy units, and emergency response scheduling is used for short-term scheduling to determine the optimal deployment of flexible energy units; A collaborative coordination module, which is used to set decision consistency constraints between the main problem and the subordinate problem, and relax and update based on this constraint; iteratively solve until a global optimal energy allocation scheme is obtained, so as to improve the resilience of the power network in an uncertain environment.

[0019] The beneficial effects of the present invention are as follows: The present invention takes into account both long-term fixed energy allocation and short-term flexible resource deployment in a unified method process, and is particularly applicable to large-scale power grid systems; by combining the N-K scenarios of renewable energy uncertainty and line faults, it can more comprehensively evaluate and enhance the anti-interference and recovery capabilities of the network in a changing environment; taking the minimization of line losses as the goal (other goals can be extended or combined), it ensures the economy and reliability of power distribution, and balances investment and operation benefits.

[0020] Furthermore, the present invention can evenly cover the probability distributions of wind speed and irradiance through Latin hypercube sampling, improving the sampling accuracy of random factors; adopting a scenario reduction algorithm can compress a large number of initial scenarios into a few representative scenarios, thereby reducing the dimension and computational complexity of the model solving process; while maintaining a relatively accurate approximation of the uncertainty distribution characteristics, the present invention significantly reduces the solving complexity and is convenient for application in large-scale systems.

[0021] Furthermore, under the N-K criterion of the present invention, the situation of multiple lines failing simultaneously is considered, enabling the system to be more resistant to external shocks (natural disasters or human attacks); after introducing "being attacked" into binary variables, different line combination faults and their impacts on power flow can be flexibly characterized in the model; by restricting the number of simultaneously faulty lines and evaluating their impacts, reliability and resilience can be specifically improved during the planning and scheduling stages.

[0022] Furthermore, the present invention realizes the discrete expression of controllable elements of the distribution network by embedding both equipment configuration and line reconfiguration decisions into the optimization model in the form of binary variables; it can not only configure different types of distributed energy units, but also switch or reconfigure lines according to the network state, which helps to adjust the operation plan in a timely manner in case of faults or high-loss scenarios; the introduction of binary variables makes this problem a mixed integer model, but it is convenient to be compatible with existing numerical optimization or distributed algorithms, so as to obtain a feasible and efficient solution.

[0023] Furthermore, the present invention fully reflects the influence of different time scales on network planning by dealing with the configuration problems of fixed resources (longer cycle) and flexible resources (short cycle or emergency stage) in stages; incorporates multiple costs such as investment costs (fixed / flexible energy units), operation losses, and fault load losses into a unified framework to avoid poor global benefits caused by local optimality; takes into account both normal operation (such as line losses) and safety and economy in extreme situations (load losses), and improves the overall resilience.

[0024] Furthermore, by minimizing the configuration cost of the fixed energy unit alone as the main problem objective, the present invention can effectively reduce over-investment or resource misallocation during the long-term planning stage; by setting a total installation quantity limit for fixed devices such as wind power and photovoltaic, it can prevent the system from blindly expanding, avoiding unnecessary economic burdens; while meeting the most basic renewable energy access requirements, it brings the investment scale and system layout within a controllable range.

[0025] Furthermore, by performing probability weighting on wind power and photovoltaic scenarios, the present invention can more accurately measure the operation performance of the system under different renewable power output levels; the present invention locks the target at minimizing the probability-weighted line loss, which helps to improve the overall energy utilization efficiency and reduce power transmission losses; it is suitable for dealing with multi-scenario large-scale problems, with more efficient calculations, and can make full use of scenario parallelism or distributed collaborative optimization methods.

[0026] Furthermore, in the scenario of multiple line failures or attacks, through flexible energy deployment and line reconstruction, the present invention minimizes the load loss to the greatest extent and improves the security of system power supply; enables the emergency dispatch to iteratively obtain a feasible solution in a short time and gradually adjust the configuration of flexible energy according to the current fault situation; in the case of partial system damage, it preferentially meets the needs of key areas or key loads, thereby improving the resilience of the distribution network in disaster or attack scenarios.

[0027] Furthermore, the present invention effectively decomposes the originally tightly coupled multiple sub-problems through the Lagrangian relaxation method, reducing the difficulty of directly solving large-scale mixed integer problems; alternately solving the master and slave problems and updating the Lagrangian multiplier can quickly narrow the solution space, ensure global consistency, and reduce the risk of the algorithm falling into local optimality; the Lagrangian relaxation or other distributed iterative algorithms are more adaptable when facing large-scale energy systems, while retaining the flexible handling ability for different scenarios or faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a schematic diagram of the method flow of the present invention; Figure 2 is a schematic diagram of the master-slave problem allocation of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, which are convenient for clearly understanding the present invention, but they do not constitute a limitation to the present invention.

[0030] Embodiment 1

[0031] As Figure 1As shown in the figure, the present invention provides an intelligent power network resilience-enhanced energy allocation method for a variable environment, including the following steps: Generate typical scenarios according to the uncertainty of the output power of wind power and photovoltaic power generation; construct a power line fault model according to the N-K criterion to simulate the extreme situation of multiple lines failing simultaneously; Considering each typical scenario and fault situation, establish an optimization model with the goal of minimizing line losses; set binary decision variables to represent the configuration status of distributed energy units and the topological reconstruction of the power grid lines; Based on the optimization model, build a two-level solution structure of the master problem and the slave problem, taking the fixed energy unit configuration and network topology decision as the master problem, and taking the scenario-driven optimization and emergency response scheduling as the two slave problems respectively; among them, the scenario-driven optimization is used for long-term planning to determine the optimal configuration of the fixed energy unit, and the emergency response scheduling is used for short-term scheduling to determine the optimal deployment of the flexible energy unit; By setting decision consistency constraints between the master problem and the slave problem, and relaxing and updating based on this constraint; iteratively solve until a global optimal energy allocation scheme is obtained, thereby improving the resilience of the power network in an uncertain environment.

[0032] Specifically, the fixed energy unit mainly includes wind turbines, photovoltaic power generation units, and fixed electrochemical energy storage devices, which are used to provide stable power generation / storage capabilities; correspondingly, the flexible energy unit can include mobile energy storage devices or small decentralized generators that can be conveniently installed / uninstalled.

[0033] This embodiment specifically includes the following steps: Step 1: Establish the wind-solar uncertainty and N-K model faced in the power network.

[0034] In actual engineering, the active power prediction of wind energy devices and photovoltaic power generation units is challenging, and their output power is mainly affected by the wind speed and irradiance at their locations. By establishing the relationship between the wind speed and wind energy devices, and the irradiance and the output active power of photovoltaic units, the actual active power of wind energy devices and photovoltaic units can be deduced. This embodiment conducts scenario-based descriptions of the output uncertainties of wind energy and photovoltaic power generation to accurately evaluate the operating status of the power network under different renewable energy output levels, and uses the sampling method of the probability model function of uncertainty variables to generate a large number of scenarios.

[0035] Specifically, first generate a large number of initial source-load planning scenarios through the Latin hypercube sampling method, and then use the backward scenario reduction method to compress the scenarios to obtain a set of typical scenarios and their corresponding scenario probabilities, so as to obtain the maximum output power of wind energy devices and photovoltaic units under different scenarios. According to the generation of long-term and short-term wind speed and irradiance scenarios, the sets and , the corresponding scenario probability is and .

[0036] Preferably, the scenario generation method adopted in this embodiment specifically includes the following steps: Under a given wind speed range, according to the power-wind speed characteristic curve of the wind energy device, determine its maximum output power; regard the active power output of the photovoltaic power generation unit as a function of solar irradiance: through the correspondence between the photovoltaic module characteristics and irradiance values, obtain the rated power of the photovoltaic unit under different irradiances. Establish statistical distributions for wind speed and irradiance respectively (such as probability density functions obtained based on historical data or prediction data) for subsequent sampling. From the probability distributions of the above wind speed and irradiance, perform Latin hypercube sampling respectively to generate a large number of initial scenario samples; in this process, in order to take into account the analysis requirements of different time scales, sampling can be implemented for the long-term and short-term wind speed and irradiance distributions respectively to obtain two types of initial scenario sets: (long-term) and (short-term). Considering that in an actual large-scale power system, the number of initial scenarios may be very large, directly adopting all scenarios will lead to a sharp increase in the amount of calculation. Therefore, this embodiment uses a backward scenario reduction algorithm to compress the initial scenario set; by measuring the similarity of different scenarios in the wind speed and irradiance dimensions and controlling the total landscape deviation, redundant scenarios are gradually eliminated and similar scenarios are merged during the reduction process; finally, a typical scenario set and Corresponding to long-term and short-term analysis, each scenario or both carry its occurrence probability and . In the scenario set, each typical scenario gives the maximum output power (or corresponding power range) of the wind energy device and the photovoltaic unit, so as to more accurately characterize the randomness of wind and light output.

[0037] Through the above method, this embodiment can significantly reduce the number of scenarios while retaining the uncertainty characteristics of wind energy and photovoltaic output, reduce the solution dimension of the subsequent optimization model, and thus improve the solution efficiency and scalability.

[0038] Preferably, to cope with large-scale power line failures or attack situations that may occur in a changing environment, this embodiment introduces the N-K criterion into the model, and the specific method is as follows: The binary variable setting for line attack is denoted as the binary variable indicating whether line is attacked: when , it means that line is in an attacked state and is regarded as a fault disconnection; if , then this line operates normally in this scenario. The combinations of attacks suffered by different lines reflect the multiple fault modes that the system may exhibit under extreme environments or malicious sabotage.

[0039] Let , where K is the maximum number of lines that can fail (or be attacked) simultaneously; this inequality is used to characterize the N-K criterion: that is, in the most adverse scenario, the system still needs to have the ability to cope with the failure of at most K lines. Through this constraint, the impact of multi-line failures on network topology and power flow distribution can be fully considered during the optimization process.

[0040] When , the line does not participate in power flow transmission, which is equivalent to removing this line from the network; correspondingly, the power flow equations need to redistribute the active and reactive power balance of each node under the remaining topology to ensure the feasibility of system operation or objectives such as minimizing load loss in the fault scenario.

[0041] Through the above modeling method, this embodiment can systematically evaluate the line fault distribution of the power grid under harsh environments or external attacks in the optimization framework, and achieve a higher safety margin and reliability at the planning and scheduling levels in combination with the N-K criterion.

[0042] Step 2: According to the theory of intelligent power network resilience enhancement strategy, establish an energy configuration model for minimizing power network losses. Based on the distributed collaborative optimization method, divide the energy configuration optimization model into a scenario-driven optimization model and an emergency response scheduling model at the model level; among them, the scenario-driven optimization model is used for long-term planning to determine the optimal configuration of fixed energy units, and the emergency response scheduling model is used for short-term scheduling to determine the optimal deployment of flexible energy units; Assume that through the binary decision variable it is indicated whether each power node installs a fixed energy unit (such as energy storage, wind turbines, photovoltaic equipment) or a mobile flexible energy storage device. If node installs the corresponding energy resource, the corresponding decision variable is 1, otherwise it is 0.

[0043] The energy configuration optimization model is a two-stage optimization model. The first stage configures fixed energy units, such as wind energy, solar energy, and fixed energy storage devices, etc.; the second stage configures flexible energy units and power grid reconstruction constraints, such as mobile energy storage systems, etc.

[0044] The objective function of this embodiment includes: the configuration cost of stable energy units , the line loss cost in scenario-driven optimization , the configuration cost of flexible energy units ( , and the load loss cost in emergency response scheduling The energy configuration optimization model is specifically as follows: ; Among them, the binary variables , , and respectively represent whether to configure distributed resources such as a fixed energy storage unit (corresponding superscript dg), a wind energy device (corresponding superscript wtg), a photovoltaic power generation unit (corresponding superscript pvg), and a flexible energy storage unit (corresponding superscript mps) at node . If configured, it is 1; otherwise, it is 0. The binary variable represents whether line is attacked. If attacked, it is 0; otherwise, it is 1. The variables , , and respectively represent the rated powers of different distributed resources configured at node . The variable l ij represents the square of the current variable in branch ij. represents the square of the current on line under scenario . The variable represents the impedance on line . The variable [[ID= 44]] represents the active power demand of the load at node . The variable represents the active power of the shed load at node under scenario . The variable represents the penalty coefficient of different load types at node . The constants , and respectively represent the unit power costs (yuan / kW) of different high-cost fixed distributed resources. The constant represents the unit power cost (yuan / kWh) of the flexible energy storage unit. The constants and respectively represent the unit cost coefficient of line loss (yuan / kW) and the unit cost coefficient of load loss (yuan / kWh); and respectively represent the probability sets under different normal operation scenarios and extreme scenarios. The sets , , , and They respectively represent the sets of high-cost fixed distributed energy configuration, power flow calculation line loss, flexible energy storage unit distributed energy configuration, line attack, and power flow calculation load loss.

[0045] The constraint conditions are as follows:

[0046]

[0047]

[0048] Among them, , and respectively represent the maximum numbers of fixed energy storage unit configuration, wind energy device, and photovoltaic power generation unit. Constraints (2)-(4) represent the quantity limitations on high-cost fixed distributed resources;

[0049]

[0050]

[0051] Among them, the binary variable represents whether line is disconnected during reconstruction. If it is disconnected, it is 0; otherwise, it is 1. The variable represents the power flow on line ; the constant represents the number of nodes; the constant M is a sufficiently large number to ensure the linearization of the constraint conditions; the sets and respectively represent the input and output line sets of node ; the sets and respectively represent the sets of existing lines and candidate lines. Constraint (5) represents that there are n - 1 effective lines in the system to ensure the connectivity of the distribution network; Constraint (6) represents the current limitation on line ij; Constraint (7) represents the power flow limitation on line

[0052]

[0053]

[0054] Among them, the superscript s1 indicates that all variables change under the long-term normal operation scenario where the variables , and represent the active power actually generated by different fixed high-cost distributed resources configured at the lower node respectively; the variables , and represent the reactive power actually generated by the corresponding distributed resources respectively; the variables and represent the active power and reactive power generated by the generator configured at the node respectively; the variables and represent the active power and reactive power on the line respectively; the variable represents the reactive power of the load cut off at the node under the scenario ; the variable represents the reactance on the line . Constraints (8) and (9) represent the active and reactive power balance constraints at the node respectively.

[0055]

[0056]

[0057] Among them, the variables and represent the voltage values at the beginning and end of the line respectively; constraints (10) and (11) represent the voltage balance constraints at the node .

[0058]

[0059]

[0060] Among them, the variables and represent the upper limits of the active and reactive power of the line respectively; constraints (12) and (13) limit the active and reactive power of the line respectively and ensure network connectivity.

[0061]

[0062]

[0063] Among them, the variables and respectively represent the upper limits of the active and reactive powers of the conventional generator configured at Node 1; Constraints (14) and (15) represent the active and reactive power limits of the generator configured at Node 1.

[0064]

[0065]

[0066] Among them, the variables and respectively represent the upper and lower limits of the voltage amplitude at Node ; The variable represents the upper limit of the square of the current on Line ; Constraint (16) represents the limitation on the voltage amplitude of Node ; Constraint (17) represents the limitation on the current on Line .

[0067]

[0068]

[0069] Among them, the variables and respectively represent the active and reactive power demands of the load at Node . Constraints (18) and (19) respectively represent the active and reactive power limits of the load shedding at Node .

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] Among them, the variables and represent the power factor angles of the configured fan and photovoltaic device respectively. Constraints (20)-(22) represent the active and reactive power output limits of the fixed energy storage device; Constraints (23)-(25) represent the active and reactive power output limits of the fan; Constraints (26)-(28) represent the active and reactive power output limits of the photovoltaic.

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] Among them, the variables 、 、 and are introduced auxiliary variables. Since the second-order cone relaxation constraint contains a quadratic equality constraint, which is inconvenient for subsequent calculations, auxiliary variables are introduced to perform second-order cone transformation on it. Constraints (29)-(32) represent the transformed second-order cone relaxation constraints.

[0086]

[0087]

[0088]

[0089]

[0090] Among them, Indicates the maximum number of mobile energy storage devices. Constraint (34) indicates the number of mobile energy storage devices. Constraint (35) indicates that there are n-1 valid lines in the system to ensure the connectivity of the distribution network; Constraint (36) indicates the number of lines. The current on the circuit is limited; constraint (37) indicates that the circuit The power flow limit on the

[0091]

[0092]

[0093]

[0094] Among them, the superscript s2 indicates that all variables are in the short-term extreme scenario The variables in the parentheses after each constraint represent the dual variables of the constraint. and Represents the next node respectively The actual active power generated by the mobile energy storage device configured above; the constant K represents the number of attack lines. Constraint (38) represents the limit on the number of attack lines; constraints (39) and (40) represent the active and reactive power balance constraints of node i, respectively.

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] Among them, the variables in the parentheses after each constraint represent the dual variables of the constraint. Constraints (41) and (42) represent the voltage balance constraints of the node ; Constraints (43) and (44) respectively limit the active power and reactive power of the line and ensure the network connectivity; Constraints (45) and (46) represent the active and reactive power limits of the generator configured at Node 1; Constraint (47) represents the limit on the voltage magnitude of the node ; Constraint (48) represents the limit on the current on the line . Constraints (49) and (50) respectively represent the active power and reactive power limits of the load shedding on the nodes . Constraints (51)-(55) represent the second-order cone relaxation constraints after introducing auxiliary variables.

[0111] The scenario-driven optimization model is used to configure high-cost stable energy units in the long-term planning framework to reduce the impact of wind and photovoltaic volatility on the line losses of the power network. The model is:

[0112] ; The emergency response scheduling model is used to configure low-cost flexible energy units in the short-term planning framework to reduce the load loss caused by the attack on the power line. The model is:

[0113] .

[0114] Step 4: Based on the optimization model, a two-level solution structure of the main problem and the sub-problem is constructed and the optimal allocation solution is obtained, such as Figure 2 shown.

[0115] Introducing three sets of variables and 、 and 、 and , respectively represent the installation locations of distributed energy units obtained according to the scenario-driven optimization model and the emergency response scheduling model, and further introduce three constraints 、 and , indicating that the solutions of the scenario-driven optimization model, long-term planning model, and short-term emergency model are equal. According to the distributed collaborative optimization algorithm, the scenario-driven optimization model can be decomposed into a master problem and two slave problems, where the master problem is:

[0116]

[0117] In the above formula, is the newly introduced variable, 、 、 and 、 、 denote the dual variables of the corresponding constraints, They represent the optimal solutions based on the scenario-driven optimization model and the emergency response scheduling model. In this problem, the variables include 、 、 and .

[0118] The following questions are asked in the context-driven optimization model:

[0119]

[0120]

[0121]

[0122] st(5)-(33); The emergency response scheduling model has the following problems:

[0123]

[0124]

[0125]

[0126] s.t. (38)-(55).

[0127] In this embodiment, a coupling mechanism for decision consistency is established between the scenario-driven optimization model and the emergency response scheduling model. By introducing constraint conditions, the configuration decisions of distributed energy units at each power node are made consistent in the scenario-driven optimization model and the emergency response scheduling model; the coupling constraints ensure that the fixed energy unit configuration scheme obtained by scenario-driven optimization and the flexible energy unit deployment scheme obtained by emergency response scheduling have the same values for the overlapping decision variables, thereby ensuring the coordination of the optimization decisions of the sub-models in different stages. The master problem and the slave problem are solved collaboratively by the method of iterative update of Lagrange multipliers: by adding the coupling constraints to the Lagrangian relaxation and introducing the corresponding Lagrange multipliers, a Lagrangian relaxation model including the master problem and the slave problem is constructed, and then the master problem and the slave problem are alternately solved and the Lagrange multipliers are updated according to the deviation of the solutions after each iteration. This cycle continues until convergence to achieve incremental collaborative optimization and solution between the master problem and the slave problem, and obtain an approximate global optimal solution that satisfies all constraints until , and .

[0128] Embodiment 2

[0129] The present invention provides an energy configuration system for enhancing the resilience of an intelligent power network facing a variable environment, which is used to implement the method described in the above technical solution, and includes: An uncertainty modeling module, which is used to perform uncertainty analysis on the outputs of wind power and photovoltaic power generation and generate a corresponding set of typical wind speed and irradiance scenarios; A fault scenario generation module, which is used to construct a power network line fault scenario based on the N-K criterion and determine the combination and quantity limit of the attacked lines under the fault scenario; An optimization model construction module, which is used to establish a distributed energy configuration optimization model with the goal of minimizing the power grid line loss, and set binary decision variables representing the installation status of distributed energy units and the power grid line reconstruction status in the model; A model decomposition module, which constructs a two - level solution structure for the master problem and the slave problems based on the optimized model. It takes the configuration of fixed - type energy units and network topology decision - making as the master problem, and takes scenario - driven optimization and emergency response scheduling as the two slave problems respectively. Among them, scenario - driven optimization is used for long - term planning to determine the optimal configuration of fixed - type energy units, and emergency response scheduling is used for short - term scheduling to determine the optimal deployment of flexible energy units. A collaborative coordination module, which is used to set decision consistency constraints between the master problem and the slave problems, and perform relaxation and update based on these constraints. Iteratively solve until a globally optimal energy configuration scheme is obtained, so as to improve the resilience of the power network in an uncertain environment.

[0130] Example 3

[0131] The present invention provides a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the energy configuration method for enhancing the resilience of an intelligent power network facing a variable environment described in the above technical solution.

[0132] Example 4

[0133] The present invention provides an electronic device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the energy configuration method for enhancing the resilience of an intelligent power network facing a variable environment described in the above technical solution.

[0134] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk memory, CD - ROM, optical memory, etc.) containing computer - usable program code.

[0135] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general - purpose computer, a special - purpose computer, an embedded processor, or other programmable data - processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data - processing devices generate for implementing in the process Figure 1 a process or multiple processes and / or blocks Figure 1a device for the functions specified in one or more boxes.

[0136] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one Figure 1 process or more processes and / or boxes Figure 1 a box or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or more processes and / or boxes Figure 1 a box or more boxes.

[0138] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention. These all fall within the protection scope of the present invention.

[0139] The content not detailedly described in this specification belongs to the known prior art of those skilled in the art.

Claims

1. An energy allocation method for enhancing the resilience of an intelligent power network facing a variable environment, characterized in that: It includes the following steps: Generate typical scenarios based on the uncertainties of wind power and photovoltaic power generation outputs; construct a power line fault model according to the N-K criterion to simulate extreme situations of multiple lines failing simultaneously; Considering each typical scenario and fault situation, establish an optimization model with the goal of minimizing line losses; Set binary decision variables to represent the configuration status of distributed energy units and the topological reconfiguration of the power grid lines; Based on the optimization model, build a two-level solution structure of the master problem and the sub-problems, taking the configuration of fixed energy units and network topology decision-making as the master problem, and taking scenario-driven optimization and emergency response scheduling as the two sub-problems respectively; among them, scenario-driven optimization is used for long-term planning to determine the optimal configuration of fixed energy units, and emergency response scheduling is used for short-term scheduling to determine the optimal deployment of flexible energy units; Set decision consistency constraints between the master problem and the sub-problems, and relax and update based on this constraint; iteratively solve until a globally optimal energy configuration plan is obtained, so as to improve the resilience of the power network in an uncertain environment.

2. The method according to claim 1, wherein The modeling of the output uncertainties of the wind power and photovoltaic power generation adopts a scenario generation method combining Latin hypercube sampling and scenario reduction to obtain a number of representative wind speed and irradiance scenarios and their occurrence probabilities, where a large number of initial scenarios are generated from the probability distributions of wind speed and solar irradiance through Latin hypercube sampling, and then the initial scenario set is compressed into a set of representative typical scenarios by using a scenario reduction algorithm.

3. The method according to claim 1, characterized in that The line fault model is established by introducing binary variables representing the attacked state of power lines, and setting a constraint that the number of attacked lines does not exceed K, which is used to characterize the situation of at most K power lines failing simultaneously in a changing environment; When the binary variable of a certain power line indicates that the line is attacked, the line is regarded as failed and disconnected to limit the power flow distribution under the corresponding fault situation in the optimization model.

4. The method according to claim 1, characterized in that, Set binary decision variables in the energy configuration optimization model to depict distributed energy configuration and power grid topology reconfiguration decisions: the first type of binary variables are used to indicate whether fixed energy units or flexible energy storage devices are installed at each power node, taking the value of 1 if installed at the corresponding node, otherwise taking the value of 0; The second type of binary variables are used to indicate the operating status of power lines, taking the value of 1 if the line is put into operation and taking the value of 0 if the line is disconnected, so as to introduce the installation selection of distributed energy units and the power grid reconstruction plan into the optimization decision-making.

5. The method according to claim 1, wherein The energy configuration optimization model is a two-stage optimization model. The first stage configures fixed energy units, and the second stage configures flexible energy units and power grid reconstruction constraints; its objective function includes the configuration cost of fixed energy units, the line loss cost of the power grid, the configuration cost of flexible energy units, and the load loss cost in extreme fault situations; the constraint conditions of the optimization model include the power flow balance constraint of the power network, the power output limits of generators and distributed energy units, the line transmission capacity limit, the upper and lower limits of node voltages, the configuration quantity constraint of fixed energy units, the configuration quantity constraint of flexible energy units, and the constraint that the number of damaged lines does not exceed K under the line fault model.

6. The method according to claim 1, wherein The main problem aims to minimize the configuration cost of the stable energy unit, and the constraints include the total installation quantity limit of fixed energy units at all nodes, the total configurable quantity limit of wind power devices, and the total quantity limit of the combined installation of photovoltaic power generation devices at each node.

7. The method according to claim 1, characterized in that The sub-problem of scenario-driven optimization aims to minimize the line loss weighted by the occurrence probabilities of each wind power and photovoltaic scenario. The constraints include the power flow equation and balance condition of the power network under normal operation, as well as the operation and capacity limits of fixed energy units, and a decomposed probabilistic optimization algorithm is used for solving to determine the optimal configuration scheme of fixed energy units at each power node.

8. The method according to claim 1, wherein The sub-problem of emergency response scheduling aims to minimize the load loss caused by the failure of the attacked line. The constraints include the power flow balance constraint of the power network in the fault scenario, the operation constraint of flexible energy units, and the power grid topology reconstruction constraint, and the deployment scheme of flexible energy units is iteratively optimized through an incremental collaborative solution algorithm.

9. The method according to claim 1, wherein A coupling mechanism for decision consistency is established between the main problem and the sub-problems, and the main problem and the sub-problems are solved collaboratively by using the method of iterative update of Lagrange multipliers; By adding the coupling constraint to the Lagrangian relaxation and introducing the corresponding Lagrange multipliers, a Lagrangian relaxation model including the main problem and the sub-problems is constructed, and then the main problem and the sub-problems are alternately solved and the Lagrange multipliers are updated according to the deviation of the solutions after each round of iteration. Such a cycle is carried out until convergence to achieve the incremental collaborative optimization among sub-models and obtain the global optimal solution that satisfies all constraints.

10. An intelligent power network resilience-enhanced energy allocation system for a variable environment, characterized in that, For implementing the method according to any one of claims 1-9, it includes: An uncertainty modeling module, which is used for performing uncertainty analysis on the outputs of wind power and photovoltaic power generation and generating the corresponding typical wind speed and irradiance scenario sets; A fault scenario generation module, which is used for constructing the power network line fault scenarios of the N-K criterion and determining the combination and quantity limit of the attacked lines in the fault scenarios; An optimization model construction module, which is used for establishing a distributed energy configuration optimization model aiming to minimize the power grid line loss, and setting binary decision variables representing the installation status of distributed energy units and the power grid line reconstruction status in the model; A model decomposition module, which builds a two-level solution structure of the main problem and the sub-problems based on the optimization model, takes the configuration of fixed energy units and network topology decision as the main problem, and takes scenario-driven optimization and emergency response scheduling as two sub-problems respectively; among them, scenario-driven optimization is used for long-term planning to determine the optimal configuration of fixed energy units, and emergency response scheduling is used for short-term scheduling to determine the optimal deployment of flexible energy units; A collaborative coordination module, which is used for setting decision consistency constraints between the main problem and the sub-problems, and performing relaxation and update based on this constraint; iteratively solving until the global optimal energy configuration scheme is obtained, so as to improve the resilience of the power network in an uncertain environment.

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