Tunnel multi-energy multi-layer optimization scheduling method and system

Abstract

The invention discloses a tunnel multi-energy multi-layer optimization scheduling method and system, and the method comprises the steps: firstly obtaining an optimization input set corresponding to a future operation cycle of a tunnel, and then constructing a multi-layer mixed integer linear programming model through employing a lexicographical order structure; and finally, based on a set constraint condition, solving the multilayer mixed integer linear programming model according to the optimized input set to obtain a plan instruction in a future operation cycle. And obtaining an optimal value for zero off-supply of the primary load through a target function of a first layer of the multi-layer mixed integer linear programming model. Under the condition that the optimal value of the first layer is locked, the optimal value with the lowest operation cost can be obtained through the target function of the second layer. And on the premise that the optimal values of the first layer and the second layer are locked, the optimal values enabling the energy storage cycle number, the unit climbing number and the switching frequency to be minimum can be obtained through the target function of the third layer. Therefore, an energy scheduling strategy with the highest economical efficiency and the longest service life of equipment can be obtained on the premise of ensuring zero off-supply of the first-level load.

Description

Technical Field

[0001] This invention relates to the technical field of scheduling methods for parallel power supply to a network by two or more power generation devices, and specifically to a multi-energy, multi-layer optimized scheduling method and system for tunnels. Background Technology

[0002] Under the current trend of transportation and energy integration, tunnels are equipped with various energy supply devices such as photovoltaic power generation, wind power generation, and energy storage batteries to meet the long-term continuous operation needs of various loads such as lighting, ventilation, and monitoring. Diesel generators are also provided to solve the power supply problem in the event of a power outage in the tunnel.

[0003] Under normal operating conditions, existing multi-energy systems typically utilize an energy management system for economic dispatching, allocating power among mains electricity, renewable energy sources, and energy storage to reduce operating costs. In emergency situations such as mains power outages, the system automatically switches to diesel generator power to ensure supply to primary loads such as emergency lighting, communication, and monitoring equipment.

[0004] However, existing energy management systems typically operate routine and emergency dispatching independently. During routine operation, the feasibility of emergency operation is not explicitly modeled, potentially leading to insufficient energy storage charge levels to support full power supply to primary loads during sudden power outages. Furthermore, energy management systems often employ single-objective optimization algorithms or weighted sums, optimizing safety, economy, and lifespan in parallel, which can easily result in sacrificing emergency lighting reliability to reduce costs. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a multi-energy, multi-layer optimized scheduling method and system for tunnels, which can simultaneously ensure both economic efficiency in normal operation and feasibility in emergency operation. The specific technical solution is as follows: In a first aspect, a multi-energy, multi-layer optimized scheduling method for tunnels is provided. In a first implementable mode of this first aspect, it includes: Obtain the optimized input set corresponding to the future operation cycle of the tunnel; A multi-level mixed-integer linear programming model with a lexicographical structure is constructed, specifically as follows: First layer: ; Second layer: ; Third layer: ; in, For the first The power consumption value of the tunnel emergency lighting during the specified time period. For the first The tunnel monitoring showed no power supply during the specified time period. For the first Time-of-use electricity pricing for different periods of the day. , These represent the comprehensive cost per unit of electricity generated by diesel power generation and the penalty coefficient for wind / solar curtailment, respectively. and The first The active power of electricity purchased from the mains and the active power of the diesel generator during the specified time period. and The first Solar power curtailment and wind power curtailment during specific time periods. For time step, , and These are the battery equivalent cycle cost factor, the penalty factor for the diesel generator's gradeability, and the penalty factor for the diesel generator's start / switching life. and These are the charging power and discharging power of the energy storage battery, respectively. The gradient of the diesel generator. ; Based on the set constraints, the multi-level mixed integer linear programming model is solved according to the optimized input set to obtain the planning instructions for future operating cycles.

[0006] In conjunction with the first possible implementation of the first aspect, in the second possible implementation of the first aspect, the optimized input set corresponding to the future operating cycle of the tunnel is obtained, including: Obtain predicted values ​​of load power, wind power output, and photovoltaic power output during the tunnel's future operating cycle; An optimized input set is generated by combining the predicted load power, wind power output, and photovoltaic power output with the corresponding equipment parameters and safety boundary values.

[0007] In conjunction with the second feasible method of the first aspect, the third feasible method of the first aspect involves obtaining the predicted values ​​of wind power output and photovoltaic power output, including: The system acquires real-time operating data of the tunnel and combines it with historical operating data to predict the wind power output and photovoltaic power output.

[0008] In conjunction with the second possible implementation of the first aspect, the fourth possible implementation of the first aspect generates an optimized input set, including: The predicted load power, wind power output, and photovoltaic power output are subjected to data cleaning and unified time step processing.

[0009] In conjunction with the first feasible method of the first aspect, in the fifth feasible method of the first aspect, the constraints include: physical and operational constraints and emergency feasibility constraints.

[0010] In conjunction with the fifth possible implementation of the first aspect, in the sixth possible implementation of the first aspect, the physical and operational constraints include: power balance constraints between power supply and load power, upper limit of available wind power generation, upper limit of available photovoltaic power generation, upper limit of available grid power, energy storage system constraints and / or generator constraints. The emergency feasibility constraints include: load bridging gap constraints, power margin constraints before startup, frequency rapid active power margin constraints, droop energy reserve constraints, and / or steady-state capability constraints after startup.

[0011] In conjunction with the fifth possible implementation of the first aspect, the seventh possible implementation of the first aspect further includes the following constraints: Constraints, reactive power constraints, and / or voltage sensitivity boundary constraints.

[0012] In conjunction with the first implementable method of the first aspect, the eighth implementable method of the first aspect also includes: Real-time monitoring of wind power generation, photovoltaic power generation, grid power, generator power, and the state of charge of energy storage systems; The power generation capacity of wind power, photovoltaic power, grid power, generator power, and the state of charge of the energy storage system are compared with the safety boundary values ​​in the planning instructions. In response to the wind power generation, photovoltaic power generation, grid power, generator power, and state of charge of the energy storage system approaching the corresponding safety boundary values, the planning instructions are fine-tuned.

[0013] In conjunction with the first implementable method of the first aspect, the ninth implementable method of the first aspect also includes: It monitors the voltage or frequency status of the mains power in real time and determines whether the mains power is interrupted based on the voltage or frequency status. In response to a mains power outage, the automatic transfer switch switches to the emergency bus and supplies power according to the emergency bridging power reference, while simultaneously driving the diesel generator to connect to the grid for power supply.

[0014] Secondly, a multi-energy, multi-layer optimized scheduling system for tunnels is provided, including: The data acquisition module is configured to acquire the optimized input set corresponding to the future operation cycle of the tunnel; The model building module is configured to construct a lexicographically ordered multi-level mixed integer linear programming model, specifically as follows: First layer: ; Second layer: ; Third layer: ; in, For the first The power consumption value of the tunnel emergency lighting during the specified time period. For the first The tunnel monitoring showed no power supply during the specified time period. For the first Time-of-use electricity pricing for different periods of the day. , These represent the comprehensive cost per unit of electricity generated by diesel power generation and the penalty coefficient for wind / solar curtailment, respectively. and The first The active power of electricity purchased from the mains and the active power of the diesel generator during the specified time period. and The first Solar power curtailment and wind power curtailment during specific time periods. For time step, , and These are the battery equivalent cycle cost factor, the penalty factor for the diesel generator's gradeability, and the penalty factor for the diesel generator's start / switching life. and These are the charging power and discharging power of the energy storage battery, respectively. The gradient of the diesel generator. ; The instruction solving module is configured to solve the multi-level mixed integer linear programming model based on the set constraints and the optimized input set to obtain the planning instructions for future running cycles.

[0015] Beneficial Effects: The tunnel multi-energy multi-layer optimization scheduling method and system of this invention, through the objective function of the first layer of a multi-layer mixed integer linear programming model with a lexicographical structure, can ensure zero power outage during the entire process of power failure and switching of primary loads such as monitoring and emergency lighting, significantly reducing the risks of frequency undershoot and voltage overshoot. The second-layer objective function can select the optimal value with the lowest operating cost. Finally, under the premise of locking the optimal values ​​of the first and second layers, the third-layer objective function can optimally determine the minimum combination of energy storage cycle, unit ramp-up, and switching times, suppressing power fluctuations and frequent switching, extending equipment life, and reducing maintenance costs. Thus, without sacrificing emergency power supply safety, it ensures the economic efficiency of the system's normal operation, while simultaneously ensuring both normal operation economics and emergency operation feasibility. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 A flowchart of a tunnel multi-energy multi-layer optimization scheduling method provided in an embodiment of the present invention; Figure 2 This is a system block diagram of a tunnel multi-energy multi-layer optimized scheduling system provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0019] like Figure 1 The flowchart shown is for a multi-energy, multi-level optimization scheduling method for tunnels. This scheduling method includes: Step 1: Obtain the optimized input set corresponding to the future operation cycle of the tunnel; Step 2: Construct a multi-level mixed integer linear programming model with a lexicographical structure; Step 3: Based on the set constraints, solve the multi-level mixed integer linear programming model according to the optimized input set to obtain the planning instructions for future operating cycles.

[0020] Specifically, firstly, the optimal input set for the tunnel's future operating cycle can be obtained. This optimal input set includes the tunnel's safety boundary values, predicted load power, predicted wind power output, and predicted photovoltaic power output, as well as the equipment parameters of each corresponding device. Then, a multi-level mixed-integer linear programming model can be constructed using a lexicographical structure. Finally, based on the set constraints, the constructed multi-level mixed-integer linear programming model can be solved according to the optimal input set to obtain the planned instructions for the future operating cycle.

[0021] In this embodiment, the constructed multi-level mixed integer linear programming model is specifically as follows: First layer: ; Second layer: ; Third layer: ; in, For the first The power consumption value of the tunnel emergency lighting during the specified time period. For the first The tunnel monitoring showed no power supply during the specified time period. For the first Time-of-use electricity pricing for different periods of the day. , These represent the comprehensive cost per unit of electricity generated by diesel power generation and the penalty coefficient for wind / solar curtailment, respectively. and The first The active power of electricity purchased from the mains and the active power of the diesel generator during the specified time period. and The first Solar power curtailment and wind power curtailment during specific time periods. For time step, , and These are the battery equivalent cycle cost factor, the penalty factor for the diesel generator's gradeability, and the penalty factor for the diesel generator's start / switching life. and These are the charging power and discharging power of the energy storage battery, respectively. The gradient of the diesel generator. , This indicates a switch has occurred. This indicates that no switch has occurred.

[0022] In this multi-level mixed-integer linear programming model with a lexicographical order structure, the first level has the highest priority. The objective function set in the first level yields the optimal value that ensures zero power outage of the primary loads in the tunnel. Primary loads can include emergency lighting and monitoring within the tunnel. While keeping the optimal value in the first level constant, the objective function in the second level yields the optimal value that minimizes the tunnel's operating cost. With the optimal values ​​in the first and second levels locked, the objective function in the third level yields the optimal value that minimizes the number of energy storage cycles, unit ramp-up times, and switching operations. Thus, the multi-level mixed-integer linear programming model achieves the most economical energy dispatch strategy with the longest equipment lifespan while ensuring zero power outage of primary loads during power failures and switching operations. It also ensures the system's economic efficiency during normal operation and its feasibility for emergency operation.

[0023] In this embodiment, optionally, obtaining the optimized input set corresponding to the future operation cycle of the tunnel includes: Obtain predicted values ​​of load power, wind power output, and photovoltaic power output during the tunnel's future operating cycle; An optimized input set is generated by combining the predicted load power, wind power output, and photovoltaic power output with the corresponding equipment parameters and safety boundary values.

[0024] Specifically, we can first obtain the predicted load power of the tunnel during its future operating cycle, as well as the predicted output power of power generation equipment such as wind turbines and solar panels during the same period. Then, we can obtain the equipment parameters for the corresponding equipment in the tunnel, such as primary load, secondary load, photovoltaic power generation equipment, energy storage equipment, etc. Finally, we can combine the equipment parameters with the predicted load power, wind power output, and photovoltaic power output to determine the safety boundary values ​​for each constraint condition.

[0025] For example, the upper limit of the primary load is determined based on the load power forecast; the lower limit of photovoltaic availability and the lower limit of wind power availability are determined based on the wind power output forecast and photovoltaic power output forecast; and the energy storage equipment is determined based on the equipment parameters of the energy storage battery. Safety lower limit. Finally, a standardized optimization input set is formed by combining all safety boundary values, wind power output prediction values, photovoltaic power output prediction values, and corresponding equipment parameters.

[0026] In this embodiment, optionally, obtaining the predicted wind power output and the predicted photovoltaic power output includes: The system acquires real-time operating data of the tunnel and combines it with historical operating data to predict the wind power output and photovoltaic power output.

[0027] Specifically, real-time operational data of the tunnel, such as the power output of photovoltaic (PV) and wind power equipment, can be collected. Combined with historical operational data, existing prediction algorithms, such as time-series prediction algorithms based on long short-term memory networks and physical-statistical hybrid prediction algorithms, can be used to predict wind and PV power output for future operating cycles. Load power predictions can also be obtained from the daily plans set by the operating unit. This provides a foundation for subsequently solving for the optimal plan instructions for future operating cycles using a multi-level mixed-integer linear programming model.

[0028] Using time series forecasting algorithms based on long short-term memory networks, physical-statistical hybrid forecasting algorithms, etc., to predict the load power of tunnels, as well as the wind power output and photovoltaic power output during future operating cycles, are conventional technical methods in this field and will not be elaborated here.

[0029] In this embodiment, optionally, generating an optimized input set includes: The predicted load power, wind power output, and photovoltaic power output are subjected to data cleaning and unified time step processing.

[0030] Specifically, when constructing the optimized input set, the obtained load power forecast, wind power output forecast, and photovoltaic power output forecast values ​​can be cleaned, and the time step of each type of forecast data can be standardized to form a normalized optimized input set. The conventional forecast values ​​in the optimized input set, such as load power forecast, wind power output forecast, and photovoltaic power output forecast, can be used for economic dispatch, while safety boundary values ​​can be used for emergency response and power quality constraints. This makes subsequent multi-level optimization more robust to forecast errors and external grid fluctuations, improving the robustness of power dispatch.

[0031] After forming a standardized optimization input set, the constructed multi-level mixed-integer linear programming model can be solved based on the optimized input set and pre-set constraints. Since the multi-level mixed-integer linear programming model can linearize the constraints, it can be solved using conventional solvers.

[0032] In this embodiment, optionally, the constraints include: physical and operational constraints and emergency feasibility constraints.

[0033] Specifically, by combining conventional physical and operational constraints with emergency feasibility constraints, it is possible to ensure that primary loads such as monitoring and emergency lighting maintain power supply throughout the entire process of power outage and switching, achieving zero power disconnection. This significantly reduces the risks of frequency undershoot and voltage overshoot, ensuring the safety of emergency operation of the power supply system.

[0034] In this embodiment, optionally, the physical and operational constraints include: power balance constraints between power supply and load power, upper limit of available wind power generation, upper limit of available photovoltaic power generation, upper limit of available grid power, energy storage system constraints and / or generator constraints. The emergency feasibility constraints include: load bridging gap constraints, power margin constraints before startup, frequency rapid active power margin constraints, droop energy reserve constraints, and / or steady-state capability constraints after startup.

[0035] Specifically, the constraints set include power balance constraints between power supply and load power, upper limit of available wind power generation, upper limit of available photovoltaic power generation, upper limit of available grid power, constraints of energy storage system, and constraints of generator.

[0036] The power balance constraint between the supply power and the load power is as follows: ; in, , The first Actual photovoltaic power utilization and actual wind power utilization during the time period , and The first The power load values ​​for emergency lighting, monitoring, and other loads in the tunnel during different time periods. For the first Power not supplied to other loads during the time period.

[0037] The constraints set can also include resource boundary constraints, specifically: , ; , ; ; in, This represents the upper limit of available wind power generation capacity. This represents the upper limit of available photovoltaic power generation. This represents the upper limit of available mains power.

[0038] The constraints set can also include energy storage system constraints, specifically: ; ; ; ; in, The charge value of the energy storage system. , These are the charging coefficient and discharging coefficient of the energy storage system, respectively. , These are the upper limits for charging power and discharging power of the energy storage system, respectively. , These are binary variables that describe the charging and discharging states of the energy storage system, respectively.

[0039] The constraints that can be set can also include generator constraints, specifically: ; ; ; , , , ; in, This is the maximum power output of the diesel generator. This refers to the maximum climbing power of the diesel generator. For the first The climbing power of the diesel generator during the time period. For the first The start / stop status of the diesel generator during a given period. This is a binary variable describing whether the diesel generator is switching states.

[0040] The emergency feasibility constraints set may include load bridging gap constraints, specifically: ; in, The net active power that the energy storage system needs to supply during a power outage. , These are the conservative usable lower limits for wind power generation and photovoltaic power generation in emergency assessments, respectively.

[0041] The emergency feasibility constraints may also include pre-start power margin constraints, specifically: ; in, This represents the power margin before startup.

[0042] The emergency feasibility constraints may also include frequency-based rapid active power margin constraints, specifically: ; in, This is the frequency-based active power margin coefficient.

[0043] The emergency feasibility constraints may also include droop energy reservation constraints, specifically: ; in, To ensure the safety factor of bridging energy, This refers to the start-up time of the diesel generator. The time it takes for the frequency to fall to its lowest point to act as a support level.

[0044] The emergency feasibility constraints may also include post-startup steady-state capability constraints, specifically: ; in, This refers to the coverage ratio for secondary loads. Secondary loads can include basic lighting loads, enhanced lighting loads, ventilation loads, and fire protection loads within the tunnel.

[0045] In this embodiment, optionally, the constraint conditions further include: Constraints, reactive power constraints, and / or voltage sensitivity boundary constraints.

[0046] Specifically, the constraints also include Constraints, reactive power constraints, and voltage sensitivity boundary constraints. Among them, The specific constraints are as follows: ; ; in, For equipment In the Active power injection during a given time period For equipment The available apparent power limit, For equipment In the Reactive power injection during a given period The minimum allowable displacement power factor, This is an index for the number of devices.

[0047] The reactive power constraint is: ; in, , The first Reactive power settings for time-of-use energy storage systems and diesel generators. This is the required lower limit value for reactive power.

[0048] The voltage sensitivity boundary constraints are limited to islanded power supply scenarios and are specifically as follows: If and only if hour, ; in, , These represent the net active and reactive power injections within the tunnel, respectively. , These are the equivalent Thevenin resistance and the equivalent Thevenin reactance, respectively. The rated voltage of the busbar To allow for voltage deviation, In terms of mains power supply status, Powering the isolated island Power outage.

[0049] By introducing Constraints, reactive power constraints, and voltage sensitivity boundary constraints can bring forward the key conditions for maintaining voltage and frequency to the planning level with minimal modeling and computational cost. This forces the equipment to reserve space for future needs. The system allows for precise positioning and upward adjustment to avoid situations where "active power is fully utilized while no active power is available" and downward surges during switching. On the other hand, it improves robustness to prediction errors and external network fluctuations. These constraints are completely linear and add almost no binary variables, making the solution more stable and faster. It can be started and stopped on demand according to the operating mode, providing safe and executable scheduling solutions in both grid-connected and islanded scenarios.

[0050] Based on the constraints and the resulting optimal input set established above, the optimal planning instructions for future operating cycles can be obtained by solving a multi-level mixed-integer linear programming model. These instructions include the active power settings for mains power, photovoltaic generators, wind turbines, energy storage devices, and diesel generators, as well as the SOC trajectory, start-up and shutdown sequence, reactive power or power factor targets, and emergency bridging power baseline curves and trigger thresholds for the energy storage system. The energy management system can then issue the required active and reactive power settings to each energy source according to these planning instructions when the future operating cycle arrives.

[0051] In this embodiment, optionally, it also includes: Real-time monitoring of wind power generation, photovoltaic power generation, grid power, generator power, and the state of charge of energy storage systems; The power generation capacity of wind power, photovoltaic power, grid power, generator power, and the state of charge of the energy storage system are compared with the safety boundary values ​​in the planning instructions. In response to the wind power generation, photovoltaic power generation, grid power, generator power, and state of charge of the energy storage system approaching the corresponding safety boundary values, the planning instructions are fine-tuned.

[0052] Specifically, while dispatching different energy sources according to planned instructions, the energy management system can also monitor the real-time power output of wind power, photovoltaic power, grid power, generator power, and the state of charge (SOC) of energy storage systems. It then compares the detected SOC values ​​with the safety boundary values ​​in the planned instructions. If these values ​​approach their respective safety boundary values—for example, wind power approaching the lower limit of wind power availability, photovoltaic power approaching the lower limit of photovoltaic availability, and the SOC of the energy storage system approaching the lower limit of the energy storage device's SOC—then the planned instructions need to be fine-tuned. This could involve starting the diesel generator earlier or reducing the charging and discharging power of the energy storage system. The goal is to achieve the lowest cost and optimal lifespan strategy based on conventional forecasts without compromising safety margins.

[0053] In this embodiment, optionally, it also includes: It monitors the voltage or frequency status of the mains power in real time and determines whether the mains power is interrupted based on the voltage or frequency status. In response to a mains power outage, the automatic transfer switch switches to the emergency bus and supplies power according to the emergency bridging power reference, while simultaneously driving the diesel generator to connect to the grid for power supply.

[0054] Specifically, during the scheduling of different energy sources according to planned instructions, the energy management system can monitor the voltage or frequency status of the mains power in real time to determine whether the mains power supply to the tunnel is interrupted. When the mains power supply is interrupted, the energy management system immediately switches the power supply to the emergency bus via an automatic transfer switch, and controls the energy storage system to immediately output power according to the planned emergency bridging power benchmark while reserving a frequency adjustment margin. This drives the diesel engine to start and connect to the grid after a delay, maintaining full power supply to the primary load and proportional power supply to the secondary load, achieving a seamless switch from normal to emergency.

[0055] like Figure 2 The diagram shown is a system block diagram of a multi-energy, multi-level optimized scheduling system for tunnels. This scheduling system includes: The data acquisition module is configured to acquire the optimized input set corresponding to the future operation cycle of the tunnel; The model building module is configured to construct a lexicographically ordered multi-level mixed integer linear programming model, specifically as follows: First layer: ; Second layer: ; Third layer: ; in, For the first The power consumption value of the tunnel emergency lighting during the specified time period. For the first The tunnel monitoring showed no power supply during the specified time period. For the first Time-of-use electricity pricing for different periods of the day. , These represent the comprehensive cost per unit of electricity generated by diesel power generation and the penalty coefficient for wind / solar curtailment, respectively. and The first The active power of electricity purchased from the mains and the active power of the diesel generator during the specified time period. and The first Solar power curtailment and wind power curtailment during specific time periods. For time step, , and These are the battery equivalent cycle cost factor, the penalty factor for the diesel generator's gradeability, and the penalty factor for the diesel generator's start / switching life. and These are the charging power and discharging power of the energy storage battery, respectively. The gradient of the diesel generator. ; The instruction solving module is configured to solve the multi-level mixed integer linear programming model based on the set constraints and the optimized input set to obtain the planning instructions for future running cycles.

[0056] Specifically, the scheduling system includes a data acquisition module, a model building module, and an instruction solving module. The data acquisition module obtains the optimized input set corresponding to the tunnel's future operating cycles. The model building module constructs the aforementioned multi-level mixed-integer linear programming model using a lexicographical structure. The instruction solving module solves the constructed multi-level mixed-integer linear programming model based on set constraints and the optimized input set, thereby obtaining the planned instructions for the future operating cycles.

[0057] The instruction solving module can obtain the optimal value for zero disconnection of primary loads in the tunnel through the first layer of the lexicographically ordered multi-level mixed-integer linear programming model constructed by the model building module. Primary loads can include emergency lighting, monitoring, etc., within the tunnel. While keeping the optimal value of the first layer unchanged, the optimal value that minimizes operating costs can be obtained through the objective function of the second layer. With the optimal values ​​of both the first and second layers locked, the optimal value that minimizes the number of energy storage cycles, unit ramp-up times, and switching times can be obtained through the objective function of the third layer. Thus, the instruction solving module, through the multi-level mixed-integer linear programming model, can obtain the most economical energy dispatch strategy with the longest equipment lifespan while ensuring zero disconnection of primary loads. Simultaneously, it ensures the economic efficiency of normal system operation and the feasibility of emergency operation.

[0058] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A multi-energy, multi-layer optimized scheduling method for tunnels, characterized in that, include: Obtain the optimized input set corresponding to the future operation cycle of the tunnel; A multi-level mixed-integer linear programming model with a lexicographical structure is constructed, specifically as follows: First layer: ; Second layer: ; Third layer: ; in, For the first The power consumption value of the tunnel emergency lighting during the specified time period. For the first The tunnel monitoring showed no power supply during the specified time period. For the first Time-of-use electricity pricing for different periods of the day. , These represent the comprehensive cost per unit of electricity generated by diesel power generation and the penalty coefficient for wind / solar curtailment, respectively. and The first The active power of electricity purchased from the mains and the active power of the diesel generator during the specified time period. and The first Solar power curtailment and wind power curtailment during specific time periods. For time step, , and These are the battery equivalent cycle cost factor, the penalty factor for the diesel generator's gradeability, and the penalty factor for the diesel generator's start / switching life. and These are the charging power and discharging power of the energy storage battery, respectively. The gradient of the diesel generator. ; Based on the set constraints, the multi-level mixed integer linear programming model is solved according to the optimized input set to obtain the planning instructions for future operating cycles.

2. The tunnel multi-energy multi-layer optimization scheduling method according to claim 1, characterized in that, Obtain the optimized input set corresponding to the future operation cycle of the tunnel, including: Obtain predicted values ​​of load power, wind power output, and photovoltaic power output during the tunnel's future operating cycle; An optimized input set is generated by combining the predicted load power, wind power output, and photovoltaic power output with the corresponding equipment parameters and safety boundary values.

3. The tunnel multi-energy multi-layer optimization scheduling method according to claim 2, characterized in that, Obtain predicted wind power output and predicted photovoltaic power output, including: The system acquires real-time operating data of the tunnel and combines it with historical operating data to predict the wind power output and photovoltaic power output.

4. The tunnel multi-energy multi-layer optimization scheduling method according to claim 2, characterized in that, Generate an optimized input set, including: The predicted load power, wind power output, and photovoltaic power output are subjected to data cleaning and unified time step processing.

5. The tunnel multi-energy multi-layer optimization scheduling method according to claim 1, characterized in that, The constraints include: physical and operational constraints and emergency feasibility constraints.

6. The tunnel multi-energy multi-layer optimization scheduling method according to claim 5, characterized in that, The physical and operational constraints include: power balance constraints between power supply and load power, upper limit of available wind power generation, upper limit of available photovoltaic power generation, upper limit of available grid power, energy storage system constraints, and / or generator constraints. The emergency feasibility constraints include: load bridging gap constraints, power margin constraints before startup, frequency rapid active power margin constraints, droop energy reserve constraints, and / or steady-state capability constraints after startup.

7. The tunnel multi-energy multi-layer optimization scheduling method according to claim 5, characterized in that, The constraints also include: Constraints, reactive power constraints, and / or voltage sensitivity boundary constraints.

8. The tunnel multi-energy multi-layer optimization scheduling method according to claim 1, characterized in that, Also includes: Real-time monitoring of wind power generation, photovoltaic power generation, grid power, generator power, and the state of charge of energy storage systems; The power generation capacity of wind power, photovoltaic power, grid power, generator power, and the state of charge of the energy storage system are compared with the safety boundary values ​​in the planning instructions. In response to wind power generation, photovoltaic power generation, grid power, generator power, or state of charge approaching the corresponding safety boundary value, the planning instructions are fine-tuned.

9. The tunnel multi-energy multi-layer optimization scheduling method according to claim 1, characterized in that, Also includes: It monitors the voltage or frequency status of the mains power in real time and determines whether the mains power is interrupted based on the voltage or frequency status. In response to a mains power outage, the automatic transfer switch is switched to the emergency bus and power is supplied according to the emergency bridging power reference, while the diesel generator is driven to connect to the grid for power supply.

10. A multi-energy, multi-layer optimized scheduling system for tunnels, characterized in that, include: The data acquisition module is configured to acquire the optimized input set corresponding to the future operation cycle of the tunnel; The model building module is configured to construct a lexicographically ordered multi-level mixed integer linear programming model, specifically as follows: First layer: ; Second layer: ; Third layer: ; in, For the first The power consumption value of the tunnel emergency lighting during the specified time period. For the first The tunnel monitoring showed no power supply during the specified time period. For the first Time-of-use electricity pricing for different periods of the day. , These represent the comprehensive cost per unit of electricity generated by diesel power generation and the penalty coefficient for wind / solar curtailment, respectively. and The first The active power of electricity purchased from the mains and the active power of the diesel generator during the specified time period. and The first Solar power curtailment and wind power curtailment during specific time periods. For time step, , and These are the battery equivalent cycle cost factor, the penalty factor for the diesel generator's gradeability, and the penalty factor for the diesel generator's start / switching life. and These are the charging power and discharging power of the energy storage battery, respectively. The gradient of the diesel generator. ; The instruction solving module is configured to solve the multi-level mixed integer linear programming model based on the set constraints and the optimized input set to obtain the planning instructions for future running cycles.

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