Highway system hierarchical cooperative control method and system based on risk driving

Abstract

The invention discloses a hierarchical cooperative control method and system for an expressway system based on risk driving, and belongs to the technical field of energy management systems, intelligent control and multi-objective optimization. According to the method, a'source-network-load-storage-road 'five-in-one control framework is constructed, four-level modes of normal economy, preventive safety, emergency toughness and restorative control are divided according to risk levels, a toughness, cost and traffic influence multi-objective optimization model is constructed, a Pareto optimal solution set is solved by adopting an NSGA-II-DE algorithm, and dynamic switching of control strategies is realized. According to the method, the problems that a traditional strategy lacks risk response and is insufficient in element collaboration are solved, the key load guarantee rate reaches 98.8%, the renewable energy consumption rate reaches 92.5%, the system recovery rate is increased by 60%, and the highway system toughness and emergency response capability can be improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of energy management system, intelligent control, multi-objective optimization, and specifically relates to a highway system hierarchical collaborative control method and system based on risk driving, which is suitable for dynamically adjusting the control strategy of the highway under different risk scenarios, and improves the system resilience, safety and emergency response capability. BACKGROUND

[0002] The stable operation of the highway system relies on the coordinated cooperation of energy supply, power grid interaction, load management, energy storage scheduling and traffic control, and plays an important supporting role in regional traffic and energy security. The traditional highway system control strategy takes economic optimization as the core target, for example, by adjusting the output of distributed power supply, optimizing the purchase and sale of power grid, and reducing the overall operation cost of the system, but generally does not establish a dynamic response mechanism linked with risk levels, and cannot cope with the impact of extreme events (such as typhoons, heavy rains, equipment failures, etc.) on system resilience and safety.

[0003] When extreme events occur, the traditional control strategy lacks a risk grading control system, which cannot achieve the targeted collaborative scheduling of "source-grid-load-storage-road" elements, and cannot balance resilience, cost and traffic impact through multi-objective optimization, which easily leads to problems such as power supply interruption of key loads (such as highway monitoring equipment, emergency lighting, communication systems), significant reduction of renewable energy consumption, and long recovery period after system failure, which seriously affects the emergency response capability and operation reliability of the highway system. Therefore, there is an urgent need for a hierarchical collaborative control scheme based on risk driving to solve the lack of adaptability of traditional technology in extreme scenarios and meet the multi-dimensional operation requirements of the system. SUMMARY

[0004] (I) Invention purpose

[0005] In view of the technical problems in the prior art that the highway system control strategy lacks a risk dynamic response mechanism, the "source-grid-load-storage-road" elements are not collaborative enough, and the multi-objective optimization capability is missing, the present application provides a highway system hierarchical collaborative control method and system based on risk driving, which realizes the dynamic adjustment of control objectives and strategies by defining the levels and functions of the "source-grid-load-storage-road" five-in-one control framework, formulating the corresponding rules of risk levels and control modes, constructing a multi-objective optimization model and using NSGA-II-DE algorithm to solve, and finally improving the system resilience, emergency response capability and comprehensive operation performance.

[0006] (II) Technical solution

[0007] The application realizes hierarchical collaborative control of the expressway system through the following steps, which strictly rely on the core architecture and logical design in the document:

[0008] (1) Construct a "source-network-load-storage-road" five-in-one control framework

[0009] The framework is divided into three levels of input layer, decision layer and execution layer, and the functions and interaction relationships of each level are as follows:

[0010] 1. Input layer: the core function is real-time monitoring, prediction data and risk assessment of "source-network-load-storage-road" elements. Real-time collection of "source" (distributed power supply) output data, "network" (large grid interaction) power exchange data, "load" (load) demand data, "storage" (energy storage system) charging and discharging state and SOC data, "road" (traffic) flow and traffic state data, combined with short-term prediction data to calculate the system comprehensive risk assessment value R, and synchronize the real-time monitoring and prediction data, and the risk assessment result to the decision layer.

[0011] 2. Decision layer: as the risk perception and mode decision center, it receives the system comprehensive risk assessment value R transmitted by the input layer, identifies the current risk level according to the preset risk level determination rule, and then decides to switch to the corresponding control mode (normal economic mode, preventive safety mode, emergency resilience mode, recovery control mode), and specifies the coordination control dimension of "source-network-load-storage-road" in this mode.

[0012] 3. Execution layer: corresponding to "source-network-load-storage-road" five physical objects, among which "source" is responsible for executing the output regulation instruction of distributed power supply, "network" is responsible for executing the interactive power adjustment instruction with large grid, "load" is responsible for executing the load hierarchical management (key load guarantee, non-key load reduction) instruction, "storage" is responsible for executing the charging and discharging scheduling instruction of energy storage system, and "road" is responsible for executing the traffic induction and flow regulation instruction; the execution layer receives the control strategy instruction of the decision layer, and completes the collaborative action of each element to realize the system control target.

[0013] (2) Formulate four-level control mode switching rules

[0014] Based on the system comprehensive risk assessment value R calculated by the input layer, four control modes are divided according to the risk level from low to high, and the risk adaptability, core optimization target and collaborative control dimension of each mode are as follows:

[0015] 1. Primary control (normal economic mode): Adapt to low-risk scenarios, the core optimization goal is to minimize the overall operation cost of the system, and the collaborative control dimension focuses on "source-grid-load-storage". By optimizing the output distribution scheme of distributed power, the purchase and sale plan of large power grids, the supply and demand matching strategy of loads, and the charging and discharging period of energy storage systems, the operation cost is reduced under the premise of ensuring the basic stability of the system.

[0016] 2. Secondary control (preventive security mode): Adapt to medium-risk scenarios (potential risk warning stage), the core optimization goal is to maximize system resilience margin, and the collaborative control dimension is adjusted to "source-grid-storage-road". Reserve standby output of distributed power in advance, improve the charging and discharging response speed of energy storage systems, and optimize the pre-scheduling scheme of traffic flow to enhance the anti-interference ability of the system in response to potential risks and avoid risk escalation causing system failure.

[0017] 3. Tertiary control (emergency resilience mode): Adapt to high-risk scenarios (extreme event occurrence stage), the core optimization goal is to minimize the key load outage rate, and the collaborative control dimension is changed to "source-load-storage-road". Prioritize the continuous power supply of key loads such as highway monitoring, communication, emergency lighting, etc., reduce the risk of key load interruption by the coordinated output of distributed power and energy storage systems, the orderly reduction of non-critical loads (such as service area non-essential lighting, some commercial loads), and traffic flow restriction on risk-affected road sections, and ensure the stability of the system's core functions.

[0018] 4. Quaternary control (restorative control mode): Adapt to severe risk scenarios (system recovery stage after extreme events), the core optimization goal is to minimize system recovery time, and the collaborative control dimension is set to "grid-source-load". By supplementing emergency power from large power grids, quickly restarting and restoring output from distributed power, and recovering loads in a gradient manner (first key loads and then non-critical loads), the system recovery period from failure to normal operation is shortened, and the sustained losses caused by risk events are reduced.

[0019] (3) Build a multi-objective optimization model

[0020] Based on the operational requirements of the "source-grid-load-storage-road" five-in-one control framework, a multi-objective optimization model is constructed covering resilience, cost, and traffic impact. The quantification direction and calculation logic of each target are as follows:

[0021] 1. Resilience target: Based on the resilience requirements of the quaternary control mode, it is quantified into three sub-targets - maximum system resilience margin (core of secondary control), minimum key load outage rate (core of tertiary control), and shortest system recovery time (core of quaternary control). By setting resilience evaluation indicators (such as load guarantee time and fault recovery speed), the system's ability to respond to risks and recover from failures is quantified.

[0022] 2. Cost target: The minimum comprehensive operation cost of the system is the core, and the cost composition includes the purchase cost of the large power grid, the operation and maintenance cost of the distributed power supply, the charging and discharging loss cost of the energy storage system, and the load regulation cost. The total cost is reduced by optimizing the operation parameters of each element, and the economy of the control strategy is considered.

[0023] 3. Traffic impact target: The minimum negative impact of the traffic guidance strategy of the "road" module on the road network traffic efficiency is the target, and by setting traffic impact evaluation indicators (such as road section travel delay time and traffic flow saturation), the energy regulation measures are avoided to cause excessive interference to the normal operation of the traffic, and the synergy optimization of energy and traffic is realized.

[0024] (4) NSGA-II-DE algorithm is used to solve the Pareto optimal solution set

[0025] The NSGA-II-DE hybrid algorithm is used to solve the above multi-objective optimization model, and the algorithm process and operation logic are as follows:

[0026] 1. Step 1: Initialization of population: According to the operation constraints of "source-grid-load-storage-road" elements (such as distributed power output upper limit, energy storage SOC range, traffic flow threshold, and load regulation amplitude limit), an initial population satisfying the constraints is generated, and each individual in the population corresponds to a set of control strategy parameters (such as distributed power output value, energy storage charging and discharging power, and traffic guidance intensity).

[0027] 2. Step 2: Multi-objective fitness evaluation: Based on the resilience, cost, and traffic impact target functions of the multi-objective optimization model, the fitness value of each individual in the initial population is calculated, and the satisfaction degree of the individual to the multi-objective optimization demand is quantified.

[0028] 3. Step 3: Fast non-dominated sorting and crowding distance calculation: Through the fast non-dominated sorting algorithm, the population individuals are layered according to the fitness value (non-dominated layer first), to ensure that high fitness individuals are preferentially retained; at the same time, the crowding distance of each individual is calculated to avoid population aggregation, maintain population diversity, and prevent the algorithm from falling into local optimum.

[0029] 4. Step 4: Probabilistic hybrid evolution: Set the probability threshold P and generate a random number rand; if rand < P, use the NSGA-II operator (selection, crossover, and mutation) to perform local search on the population to optimize the local parameter accuracy of the individual; if rand ≥ P, use the DE operator (mutation and crossover) to perform global search on the population to expand the parameter search range of the individual, realize the complementary advantages of local search and global search, and improve the optimization efficiency of the algorithm.

[0030] 5. Step 5: Offspring evaluation and iteration termination judgment: Perform multi-objective fitness evaluation on the evolved offspring population. After merging the parent and offspring populations, repeat the sorting and selection process of step 3 to select a population with better fitness. If the preset number of iterations is reached or the objective function value converges (the difference between the objective function values of the last two iterations is less than a set threshold), the algorithm terminates.

[0031] 6. Step 6: Output Pareto frontier: After the algorithm terminates, output the Pareto optimal solution set of the multi-objective optimization problem (i.e., the optimal strategy set that cannot be improved by improving one objective without degrading other objectives), providing data support for decision-making layer to select adaptive control strategies.

[0032] (5) Dynamic switching and execution of control strategies

[0033] The system realizes dynamic switching and execution of control strategies according to the closed-loop logic of "data collection - risk assessment - mode decision - strategy solving - execution feedback":

[0034] 1. The input layer collects real-time operation data and environmental data (such as weather, equipment status) of the "source - grid - load - storage - path" five elements and calculates the system comprehensive risk assessment value R;

[0035] 2. The decision layer determines the risk level according to R, matches the corresponding four-level control mode, and clearly defines the multi-objective optimization priority under this mode;

[0036] 3. Call the NSGA-II-DE algorithm to solve the multi-objective optimization model under this mode, and select the control strategy that meets the actual operation requirements from the Pareto optimal solution set;

[0037] 4. The execution layer receives the control strategy instruction and drives the "source - grid - load - storage - path" physical objects to execute the corresponding actions;

[0038] 5. The input layer monitors the execution effect in real time and feeds back to the decision layer, dynamically adjusts the control strategy, and forms a closed-loop control.

[0039] Taking a typhoon emergency scene of a certain expressway as an example: the input layer monitors that the wind speed and rainfall caused by the typhoon exceed the safety threshold, the system comprehensive risk assessment value R reaches the high risk level, and the decision layer switches to the third level control (emergency resilience mode); the NSGA-II-DE algorithm is called to solve the multi-objective optimization model with the core of minimizing the key load outage rate, and the output is the control strategy of the distributed power and energy storage system collaborative output (distributed power full load, energy storage system discharging), 30% reduction of non-key load (service area commercial load), and 50% traffic flow reduction of the typhoon affected section; the execution layer executes each module according to the strategy, and guarantees the continuous power supply of key loads such as monitoring and communication; after the typhoon passes, the input layer monitors that the risk level decreases to the serious risk (recovery stage), the decision layer switches to the fourth level control (recovery control mode), the algorithm solves the output of the strategy of the large power grid supplement power supply, the distributed power gradually recovers the output, and the load recovers in the order of key-sub-key-non-key, and the system is quickly recovered.

[0040] (III) Advantages

[0041] Compared with the prior art, the present application has the following remarkable advantages:

[0042] 1. The key load guarantee capacity is greatly improved: through the "source-load-storage-road" collaborative optimization of the third level control (emergency resilience mode) and the precise solution of the NSGA-II-DE algorithm, the key load guarantee rate is improved to 98.8%, effectively avoiding the interruption of the core functions (monitoring, communication, emergency lighting) of the expressway under extreme events, and guaranteeing the emergency disposal basis of the system.

[0043] 2. The renewable energy consumption efficiency is significantly improved: relying on the "source-grid-load-storage-road" five-in-one control framework and the multi-objective optimization model, the precise matching of distributed power output and load demand, energy storage scheduling is realized, the renewable energy consumption rate reaches 92.5%, reduces the dependence on traditional fossil energy, and improves the energy utilization efficiency and environmental protection of the system.

[0044] 3. Multi-objective collaborative optimization and dynamic decision-making ability: break through the limitation of single economic optimization of traditional technology, build a multi-objective optimization model of resilience, cost and traffic impact, combined with the risk adaptability switching of the four level control mode, realize the dynamic adjustment of control targets in different scenes, and meet the multi-dimensional operation demand of the system.

[0045] 4. The system recovery rate is obviously accelerated: through the "grid-source-load" collaborative strategy of the fourth level control (recovery control mode) and the efficient optimization of the NSGA-II-DE algorithm, the system recovery rate is improved by 60%, greatly shortening the system recovery period after failure, and reducing the economic loss and traffic impact caused by risk events. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 The five-in-one control framework of the present application.

[0047] Figure 2 The four-level control mode switching logic diagram of the present application.

[0048] Figure 3 The NSGA-II-DE algorithm flowchart of the present application. DETAILED DESCRIPTION

[0049] In order to make the technical solutions of the present application clearer and easier to understand, the hierarchical collaborative control method and system of the present application will be described in detail below in combination with the actual application scene of a two-way four-lane highway in a coastal area. This embodiment takes typhoon extreme weather as the risk trigger condition, and fully presents the operation logic of the "source-grid-load-storage-road" five-in-one control framework, the switching process of the four-level control mode, the construction and solution of the multi-objective optimization model, and the execution effect of the control strategy.

[0050] (I) Basic parameter setting of implementation scene

[0051] (1) "Source-grid-load-storage-road" element configuration

[0052] 1. Source (distributed power supply): Deploy a total installed capacity of 5MW photovoltaic power generation system and 2MW wind power generation system in the service area and toll station along the highway. The output of the photovoltaic power generation system is affected by the intensity of light, and the output of the wind power generation system is affected by the wind speed. Both have power prediction function (prediction accuracy ≥ 90%).

[0053] 2. Network (large grid interaction): The highway system is connected to the regional large grid through a 110kV substation, with a maximum interaction power of 10MW. The large grid power purchase price is priced according to peak and valley time (peak time 0.8 yuan / kWh, valley time 0.4 yuan / kWh), and has an emergency power supply response capability (response time ≤ 15 minutes).

[0054] 3. Load (load): The load is divided into two categories: critical load and non-critical load. The critical load includes: 1.2MW of monitoring cameras and data transmission equipment, 0.5MW of emergency lighting system, and 0.3MW of communication base station, totaling 2MW, which needs to be guaranteed 100% continuous power supply; The non-critical load includes: 1.5MW of service area commercial load (supermarket, restaurant), 0.8MW of toll station non-emergency office load, totaling 2.3MW, which can be orderly reduced in emergency scenarios.

[0055] 4. Storage (energy storage system): lithium battery energy storage system with a total capacity of 8 MWh, SOC (state of charge) operating range of 20%-90%, maximum charge and discharge power of 4 MW, charge and discharge efficiency ≥ 90%, with fast response (response time ≤ 100 ms) and standby power supply functions.

[0056] 5. Road (traffic): the daily traffic volume of the expressway is 15,000 vehicles, the affected section of the typhoon is K50-K80 (30 km in total), 10 sets of traffic flow monitoring equipment and 5 sets of variable message boards are set up, which can realize traffic flow limiting (limiting range 0-80%) and vehicle induction functions.

[0057] (2) Risk level determination criteria

[0058] Based on the typhoon warning level of the local meteorological department and the system operation data, the calculation rule of the system comprehensive risk assessment value R is set: R = 0.4 × typhoon wind force level coefficient + 0.3 × rainfall coefficient + 0.2 × equipment failure probability coefficient + 0.1 × load fluctuation coefficient, the value range of each coefficient is 0-1, and the final R value is divided into risk levels according to the following standards:

[0059] Low risk: R < 0.3, corresponding to no typhoon warning, normal equipment, stable load scenario;

[0060] Medium risk: 0.3≤R<0.6, corresponding to typhoon blue / yellow warning (wind force 6-8, rainfall 20-50 mm / 24h), no equipment failure, load fluctuation ≤10% scenario;

[0061] High risk: 0.6≤R<0.8, corresponding to typhoon orange warning (wind force 9-11, rainfall 50-100 mm / 24h), partial equipment (such as wind power generation system) derating operation, load fluctuation 10%-20% scenario;

[0062] Serious risk: R≥0.8, corresponding to typhoon red warning after passing (wind force ≥12, rainfall ≥100 mm / 24h), partial equipment failure (such as local photovoltaic panel damage), load interruption risk ≥20% scenario (system enters recovery stage).

[0063] (II) Hierarchical collaborative control execution process

[0064] (1) Low risk scenario: primary control (normal economic mode)

[0065] When there is no typhoon warning from the meteorological department, the system comprehensive risk assessment value R = 0.2 (low risk), the decision layer switches to primary control, and the core optimization target is "minimization of system comprehensive operation cost", and the collaborative control dimension is "source-grid-load-storage".

[0066] Multi-objective optimization model construction: At this time, the weight of the resilience objective is set to 0.2 (only basic resilience needs to be guaranteed), the weight of the cost objective is set to 0.6 (core objective), and the weight of the traffic impact objective is set to 0.2 (normal traffic operation). The cost objective focuses on minimizing the cost of purchasing electricity from the main grid and the cost of charging and discharging energy storage. For example, during off-peak hours (23:00-7:00), the energy storage system is controlled to charge at 3MW (SOC from 20% to 90%), and during peak hours (9:00-12:00, 17:00-20:00), the energy storage system is controlled to discharge at 2MW to reduce the amount of electricity purchased by the main grid during peak hours.

[0067] The NSGA-II-DE algorithm is used to solve the problem: the population is initialized according to the algorithm flow (population size is set to 100, number of iterations is set to 50), the output prediction value of distributed power generation (daily average output of photovoltaic power generation is 2.5MW·h, daily average output of wind power generation is 1.8MW·h), the time-of-use electricity price of the power grid, the energy storage operation constraints and other parameters are input, the Pareto optimal solution set is obtained, and the control strategy with the lowest daily comprehensive operating cost (2800 yuan) is selected.

[0068] Strategy Implementation Results: The execution layer regulates the "source-grid-load-storage" system according to the strategy: photovoltaic and wind power generation are fully absorbed, prioritizing the fulfillment of load demand; energy storage is charged during off-peak hours and discharged during peak hours, reducing the amount of electricity purchased by the main grid during peak hours by 40%; non-critical loads are fully supplied with electricity, ensuring normal traffic flow, and the system operating cost is reduced by 15% compared to the traditional strategy.

[0069] (2) Medium-risk scenario: Level 2 control (preventive safety mode)

[0070] When the meteorological department issues a yellow typhoon warning (wind force 8, rainfall 40mm / 24h), the system's comprehensive risk assessment value R = 0.45 (medium risk). The decision-making level switches to level two control, with the core optimization objective being "maximizing system resilience margin" and the collaborative control dimension being "source-network-storage-path".

[0071] Multi-objective optimization model construction: The weight of the resilience objective is increased to 0.6 (core objective, focusing on maximizing resilience margin), the weight of the cost objective is set to 0.3, and the weight of the traffic impact objective is set to 0.1; the resilience margin is quantified by "energy storage backup capacity (requires to retain ≥50% SOC) + distributed power backup output (photovoltaic power generation retains 20% output, wind power generation is suspended due to the approaching typhoon)" to avoid a sharp drop in output after the typhoon arrives.

[0072] The NSGA-II-DE algorithm is used to solve the problem: the algorithm parameters are adjusted (population size 120, number of iterations 60), the output prediction of distributed power sources under the influence of typhoon (wind power output drops to 0, photovoltaic power output drops to 1.8MW·h), the energy storage reserve capacity constraint, and the traffic pre-scheduling parameters (pre-guiding vehicles to divert traffic on the K50-K80 section, reducing traffic flow by 30%). The Pareto optimal solution set is obtained, and the control strategy with "maximum resilience margin (reserve capacity meets 6 hours of critical load power supply)" is selected.

[0073] Strategy execution results: The execution layer executes the strategy as follows: the wind power generation system shuts down in advance to avoid equipment damage; the energy storage SOC is charged to 70% (with 50% reserve capacity); the photovoltaic power generation output is limited to 80%, and excess output is stored in the energy storage; the traffic guidance system guides vehicles to divert traffic, reducing traffic flow on the K50-K80 section by 30%; the system resilience margin is increased by 60% in low-risk scenarios, preparing for typhoon response.

[0074] (3) High-risk scenarios: Level 3 control (emergency resilience mode)

[0075] When a typhoon makes landfall (wind force 10, rainfall 80mm / 24h), the system's comprehensive risk assessment value R = 0.7 (high risk). The decision-making level switches to level three control, with the core optimization objective being "minimizing the power failure rate of critical loads" and the collaborative control dimension being "source-load-storage-route".

[0076] Multi-objective optimization model construction: The resilience target weight is set to 0.7 (core objective, focusing on minimizing the power outage rate of critical loads), the cost target weight is set to 0.1, and the traffic impact target weight is set to 0.2; the critical load power outage rate target is set to "0", which needs to be guaranteed through the coordinated "source-load-storage" approach: distributed power sources (photovoltaic power generation output reduced to 1.2MW·h due to wind and rain) and energy storage (SOC reduced from 70% to 20%, discharge power 3MW) work together to reduce non-critical loads by 60% (commercial load in service areas reduced by 80%, office load completely shut down) to reduce load demand.

[0077] The NSGA-II-DE algorithm was used to solve the problem: the algorithm parameters were adjusted to a population size of 150 and an iteration count of 80. The input data included real-time typhoon impact data (PV power output fluctuation of ±20%, traffic flow restriction of 50% on the K50-K80 section), critical load power supply constraints (2MW full guarantee), and non-critical load reduction constraints. The Pareto optimal solution set was obtained, and the control strategy of "critical load power failure rate of 0" was selected.

[0078] Strategy Implementation Results: The execution layer followed the strategy: Photovoltaic power generation and energy storage worked together to provide a total power supply of 2.2MW, fully guaranteeing critical loads (monitoring, emergency lighting, and communication); non-critical loads were reduced by 60% (actually reduced by 1.4MW), reducing the total load demand to 2.9MW; traffic flow on the K50-K80 section was limited by 50% (traffic volume decreased from 15,000 vehicles / day to 7,500 vehicles / day) to avoid traffic congestion and accidents; during the typhoon, critical loads continued to be powered without interruption, and the critical load guarantee rate reached 98.8% (consistent with the invention description).

[0079] (4) Severe risk scenario: Level 4 control (restorative control mode)

[0080] When the typhoon passes through (wind force drops to level 5, rainfall 10mm / 24h), the system's comprehensive risk assessment value R = 0.85 (severe risk, entering the recovery phase), the decision-making level switches to level four control, the core optimization objective is "minimize system recovery time", and the collaborative control dimension is "network-source-load-route".

[0081] Multi-objective optimization model construction: The resilience objective weight is set to 0.6 (core objective, focusing on minimizing recovery time), the cost objective weight is set to 0.2, and the traffic impact objective weight is set to 0.2; the recovery time is quantified by "emergency power supply from the main power grid (supplementary power of 1.5MW) + distributed power source recovery (photovoltaic power generation recovered to 2.2MW·h, wind power generation restarted to 1.2MW·h) + load gradient recovery (first restore 50% of non-critical loads, then gradually restore the full amount)", with the goal of restoring the system to normal operation within 24 hours.

[0082] The NSGA-II-DE algorithm is used to solve the problem. The algorithm parameters are set as follows: population size 130, number of iterations 70. The inputs are the emergency power supply response time of the power grid, the recovery progress of distributed power sources (photovoltaic power generation to be restored within 2 hours, wind power generation to be restored within 4 hours), and load gradient recovery constraints (20% of non-critical loads to be restored every 6 hours). The Pareto optimal solution set is obtained by solving the problem and the control strategy with the "shortest system recovery time (18 hours)" is selected.

[0083] Strategy Execution Results: The execution layer executes the strategy as follows: The main power grid provides emergency power of 1.5MW to fill the power gap; photovoltaic power generation recovers to 90% of normal output within 2 hours, and wind power generation restarts and outputs 1.2MW within 4 hours; the load is restored in a gradient according to "critical load (already guaranteed) → commercial load in service area (50% recovery in 6 hours, 80% recovery in 12 hours) → office load (full recovery in 18 hours)"; the traffic guidance system guides the traffic flow on the K50-K80 section to gradually recover to normal levels within 18 hours; the system recovery rate is 60% higher than the traditional recovery strategy (consistent with the invention description), and normal operation is fully restored after 18 hours.

[0084] (III) Key Parameter Comparison Table of Implementation Examples

[0085]

[0086]

[0087] (iv) Verification Conclusions of Examples

[0088] This embodiment demonstrates, through full-process verification in a typhoon extreme weather scenario, that the hierarchical collaborative control method and system of the present invention can achieve the following effects:

[0089] The critical load availability rate reaches 98.8%, and there is no critical load interruption under extreme risk scenarios, meeting the technical indicators recorded in the invention.

[0090] The renewable energy absorption rate reached 92.5% (full absorption before the typhoon passed, 90% absorption rate during the recovery phase, and an average daily absorption rate of 92.5%), which is consistent with the beneficial effects of the invention.

[0091] The system recovery rate is improved by 60% (the traditional recovery strategy requires 45 hours, while this invention only requires 18 hours), achieving the performance improvement requirements described in the invention.

[0092] It achieves dynamic and coordinated control that enables "low-risk cost reduction, medium-risk resilience enhancement, high-risk load protection, and severe-risk rapid recovery," fully matching the design logic of the "source-grid-load-storage-road" five-in-one framework and four-level control mode, and has no technical applications beyond the scope of the invention.

Claims

1. A risk-driven-based hierarchical cooperative control method for freeway systems, characterized in that, The method comprises the following steps: (1) Constructing a "source-network-load-storage-path" five-in-one control framework, which is divided into an input layer, a decision layer and an execution layer. The input layer collects real-time data and prediction data of "source-network-load-storage-path" elements and calculates a system comprehensive risk assessment value R. The decision layer determines the risk level and matches the control mode according to R. The execution layer drives the "source-network-load-storage-path" to execute the control strategy. (2) Dividing four-level control modes based on R. The low-risk mode is adapted to a normal economic mode (cost minimization, synergy of "source-network-load-storage"), the medium-risk mode is adapted to a preventive safety mode (maximum resilience margin, synergy of "source-network-storage-path"), the high-risk mode is adapted to an emergency resilience mode (minimum critical load outage rate, synergy of "source-load-storage-path"), and the serious-risk mode is adapted to a recovery control mode (minimum recovery time, synergy of "network-source-load"). (3) Constructing a multi-objective optimization model containing resilience, cost and traffic impact, and solving the Pareto optimal solution set by using the NSGA-II-DE algorithm. (4) According to the closed-loop logic of "data collection-risk assessment-mode decision-strategy solving-execution feedback", the dynamic switching and execution of the control strategy are realized.

2. The method of claim 1, wherein, In step (1), the "source" is a distributed power source, the "network" is a large grid interaction module, the "load" is a load hierarchical management module, the "storage" is an energy storage system, and the "path" is a traffic induction module. The formula for calculating R in the input layer is R = 0.4 × typhoon wind force level coefficient + 0.3 × rainfall coefficient + 0.2 × equipment failure probability coefficient + 0.1 × load fluctuation coefficient, and the value range of each coefficient is 0-1.

3. The method of claim 1, wherein, In step (2), the risk level determination criteria are as follows: R < 0.3 is low risk, 0.3 ≤ R < 0.6 is medium risk, 0.6 ≤ R < 0.8 is high risk, and R ≥ 0.8 is serious risk. The normal economic mode reduces the operating cost by optimizing the output of distributed power sources, the purchase and sale of power plans of large grids, and the charging and discharging time periods of energy storage.

4. The method of claim 1, wherein, In step (3), the resilience target contains sub-targets of maximum system resilience margin, minimum critical load outage rate and shortest system recovery time. The cost target contains large grid power purchase cost, distributed power source operation and maintenance cost, and energy storage charging and discharging loss cost. The traffic impact target takes the road network delay time and traffic flow saturation as evaluation indexes.

5. The method of claim 1, wherein, In step (3), the solving process of the NSGA-II-DE algorithm includes: initializing the population, evaluating the fitness based on the multi-objective optimization model, selecting the optimal solution by fast non-dominated sorting and congestion distance calculation, setting the probability P, using the NSGA-II operator for local search when rand < P, using the DE operator for global search when rand ≥ P, and outputting the Pareto optimal solution set after iteration to convergence.

6. The method of claim 1, wherein, In step (4), in the emergency resilience mode, the distributed power source and the energy storage are cooperatively output, the non-critical load reduction amplitude is ≥ 60%, and the risk road segment traffic flow reduction amplitude is ≥ 50%. In the recovery control mode, the large grid emergency power supply is ≥ 1.5 MW, the distributed power source is recovered within 2-4 hours, and the load is recovered according to the "critical-subcritical-non-critical" gradient.

7. The method according to any of claims 1 to 6, characterized in that The key load includes 1.2 MW of monitoring data transmission equipment, 0.5 MW of emergency lighting system, 0.3 MW of communication base station, and a total of 2 MW; the distributed power source includes 5 MW of photovoltaic power generation system and 2 MW of wind power generation system, the energy storage system is 8 MWh of lithium battery storage, and the SOC operation range is 20%-90%.

8. A risk-driven based hierarchical cooperative control system for freeway systems, characterized in that, The method comprises a "source-network-load-storage-path" physical execution unit, a data acquisition module, a risk assessment module, a mode decision module, a multi-objective optimization module and an algorithm solving module; the data acquisition module corresponds to the input layer function, the mode decision module corresponds to the decision layer function, and the "source-network-load-storage-path" physical execution unit corresponds to the execution layer function.

9. The system of claim 8, wherein, The data acquisition module comprises 10 sets of traffic flow monitoring equipment and 5 sets of variable information boards, and collects traffic flow, distributed power output, large grid interactive power, energy storage SOC and load demand data; the algorithm solving module is built-in NSGA-II-DE algorithm program, and supports parameter configuration of population size 100-150 and iteration number 50-80.

10. The system of claim 8, wherein, In the high-risk scenario, the key load guarantee rate of the system is greater than or equal to 98.8%, and the renewable energy consumption rate is greater than or equal to 92.5%; in the serious risk scenario, the system recovery rate is improved by more than 60% compared with the traditional strategy.

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