Coal mine energy system electric-thermal-coal layered rolling robust safety scheduling method

Abstract

This invention provides a robust and safe scheduling method for a coal mine energy system with tiered rolling operation, adapting to differences in the response times of the physical processes of electricity, heat, and coal. Based on these differences, a multi-timescale tiered rolling optimization framework is constructed. Physical processes with different response speeds correspond to different rolling optimization levels at different time scales. The optimization results at slower time scales serve as boundary conditions or reference trajectories for faster time scale optimization. During the current optimization period at each time scale, the safety constraint boundaries of the physical quantities corresponding to that time scale are dynamically corrected based on the uncertainty disturbance characteristics of that time scale. A scheduling plan is generated based on the optimization framework and the dynamically corrected safety constraint boundaries of the coal bunker at the bottom of the mine, heating temperature, and node voltage. This invention achieves robust and safe scheduling that adapts to differences in the response times of power supply, heating, and coal transportation while resisting uncertainty disturbances, effectively improving the safety and robustness of system operation with only a small economic cost.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization and operation technology, specifically referring to the robust and safe scheduling method of electricity-heat-coal layered rolling in coal mine energy systems. Background Technology

[0002] Currently, in the field of integrated energy system optimization and operation technology, the operation mode of coal mine energy systems is gradually shifting from the traditional extensive function to a refined scheduling mode involving multi-network coupling and multi-link coordination. However, existing scheduling methods typically employ single-time-scale scheduling commands, which are difficult to adapt to the deviations caused by the continuous accumulation of external environmental changes and internal production disturbances during actual operation. On the one hand, significant uncertainties exist on both the source and load sides of the system, including random fluctuations in raw coal production, outdoor temperature prediction errors, and drastic changes in new energy output and impulsive electrical loads. On the other hand, coal mine electricity consumption, heating, and coal transportation exhibit significant thermal inertia and delay effects, while coal mining and transportation show a relatively long time cycle. If single-time-scale scheduling commands are still used during the intraday operation phase, it will not only be difficult to eliminate the power imbalance caused by multiple uncertainties, but it will also easily lead to serious safety risks such as coal bunker overflow / empty bunkers at the bottom of the mine, exceeding the limit of shaft antifreeze temperature, and drastic voltage fluctuations in the distribution network. Therefore, there is an urgent need for a multi-time-scale robust scheduling method that can take into account the differences in response to multiple processes and the safety of uncertain disturbances. Summary of the Invention

[0003] This invention provides a robust and safe scheduling method for a coal mine energy system with a tiered rolling system for electricity, heat, and coal. This method can achieve robust and safe scheduling that adapts to the differences in the response of coal mine power supply, heating, and coal transportation, while resisting uncertainties and disturbances in each link.

[0004] To achieve the above objectives, the present invention provides a robust safety scheduling method for the electricity-heat-coal layered rolling process in a coal mine energy system. Based on the differences in response time of the various physical processes of electricity, heat, and coal in the coal mine energy system, a multi-timescale layered rolling optimization framework is constructed. In this framework, physical processes with different response speeds correspond to rolling optimization levels at different time scales, and the optimization results at the slow time scale are used as boundary conditions or reference trajectories for optimization at the fast time scale.

[0005] During the current optimization period at each time scale, the safety constraint boundaries of the bottom coal bunker, heating temperature, and node voltage at that time scale are dynamically corrected based on the uncertainty disturbance characteristics corresponding to that time scale.

[0006] Based on the multi-timescale hierarchical rolling optimization framework and the dynamically corrected safety constraints of the coal bunker at the bottom of the mine, heating temperature, and node voltage, a scheduling plan for the coal mine energy system is generated.

[0007] As a further aspect of the present invention: the multi-timescale hierarchical rolling optimization framework includes a long-timescale rolling optimization level, a short-timescale rolling optimization level, and a real-time dynamic-scale rolling optimization level; slow-response physical processes correspond to the long-timescale rolling optimization level, medium-response physical processes correspond to the short-timescale rolling optimization level, and fast-response physical processes correspond to the real-time dynamic-scale rolling optimization level; the coal transportation plan generated by the long-timescale rolling optimization level serves as the fixed boundary between the short-timescale rolling optimization level and the real-time dynamic-scale rolling optimization level, and the generated power supply and heating operation plans serve as the reference trajectories between the short-timescale rolling optimization level and the real-time dynamic-scale rolling optimization level; the heating operation plan generated by the short-timescale rolling optimization level serves as the fixed boundary between the real-time dynamic-scale rolling optimization level, and the generated power supply operation plan serves as the reference trajectory of the real-time dynamic-scale rolling optimization level.

[0008] As a further aspect of the present invention: the slow-response physical process is a coal transportation process with energy storage and buffering characteristics, the medium-response physical process is a heat transmission process with thermal inertia, and the fast-response physical process is a power transmission process with instantaneous balance characteristics.

[0009] As a further aspect of the present invention: the correspondence between the uncertainty disturbance characteristics and the physical quantity safety constraint boundary includes: the cumulative disturbance of raw coal mining and the coal storage boundary corresponding to the long time scale; the outdoor temperature environment disturbance and the shaft antifreeze temperature operation boundary corresponding to the short time scale; and the impact electrical load disturbance and the distribution network node voltage operation range boundary corresponding to the real-time dynamic scale.

[0010] As a further aspect of the present invention: during the current optimization period on the stated long-term timescale, the maximum deviation value of coal storage is calculated based on the upper limit of the cumulative disturbance of raw coal mining, and the upper and lower limits of coal storage are tightened based on the maximum deviation value of coal storage; the upper limit of the cumulative disturbance of raw coal mining is the upper limit of the predicted deviation of the fully mechanized mining face output. The maximum deviation of the coal storage capacity The calculation formula is:

[0011]

[0012] In the formula, To be fed into the secondary coal bunker The collection of fully mechanized mining faces The unit dispatch interval is on a long-term timescale; the tightened upper and lower limits of coal storage capacity are:

[0013]

[0014] In the formula, for auxiliary coal bunker Coal reserves; , These are the secondary coal bunkers. The maximum and minimum capacity.

[0015] As a further aspect of the present invention: during the current optimization period on the short timescale, the upper limit of the wellbore antifreeze temperature deviation is calculated based on the upper limit of the outdoor temperature environment disturbance, and the upper and lower limit constraints of the wellbore antifreeze temperature are tightened based on the upper limit of the wellbore antifreeze temperature deviation; the upper limit of the outdoor temperature environment disturbance is the upper limit of the deviation of the actual outdoor temperature relative to the rolling prediction value. The wellbore antifreeze temperature deviates from the upper limit. The calculation formula is:

[0016]

[0017] In the formula, The soil heat transfer coefficient, Let w be the heat transfer area of ​​the wellbore. The scheduling interval is a short-time unit. The specific heat capacity of air, air density, Let w be the air volume of the wellbore; the upper and lower limits of the wellbore's antifreeze temperature after tightening are:

[0018]

[0019] In the formula, for Time well The temperature of the internal air, wellbore The upper and lower limits of the internal air temperature.

[0020] As a further aspect of the present invention: during the current optimization period at the real-time dynamic scale, the maximum voltage drop amplitude is calculated based on the upper limit of the power surge of the impulsive electrical load disturbance and the voltage sensitivity matrix, and the lower limit constraint of the distribution network node voltage is tightened based on the maximum voltage drop amplitude; the voltage sensitivity matrix includes the sensitivity coefficient of the node voltage amplitude to the active power injected at the connection point. and the sensitivity coefficient to reactive power injection at nodes The formula for calculating the voltage deviation caused by node power deviation is:

[0021]

[0022] In the formula, Let be the voltage deviation at node i. and These represent the injected active and reactive power deviations at node j, respectively; the upper limit of the power surge of the sudden charge disturbance includes the upper limit of the active power surge of the impulsive load. and the upper limit of sudden increase in reactive power The maximum voltage drop The calculation formula is:

[0023]

[0024] In the formula, The set of nodes connected to impulsive loads; the tightened lower limit constraint for distribution network node voltage is:

[0025]

[0026] In the formula, for Time Node voltage, They are nodes The lower and upper limits of the allowable voltage.

[0027] The present invention also provides an electronic device, including a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the steps of the coal mine energy system electricity-heat-coal stratified rolling robust safety scheduling method as described above.

[0028] As a further aspect of the present invention: the electronic device includes a multi-timescale hierarchical rolling optimization module and a dynamic safety margin correction module; the multi-timescale hierarchical rolling optimization module is configured to construct a multi-timescale hierarchical rolling optimization framework based on the response time differences of various physical processes of electricity, heat, and coal in the coal mine energy system, and generate a scheduling plan for the coal mine energy system based on the multi-timescale hierarchical rolling optimization framework and the dynamically corrected physical quantity safety constraint boundary; the dynamic safety margin correction module is configured to dynamically correct the physical quantity safety constraint boundary corresponding to each time scale during the current optimization period of each time scale, based on the uncertainty disturbance characteristics corresponding to that time scale.

[0029] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the robust and safe scheduling method for the electricity-heat-coal stratified rolling mechanism of a coal mine energy system as described above.

[0030] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs a multi-timescale hierarchical rolling optimization framework based on the response time differences of various physical processes (electricity, heat, and coal) in a coal mine energy system. This allows physical processes with different response speeds to correspond to different rolling optimization levels at different time scales, and the optimization results at slower time scales serve as boundary conditions or reference trajectories for faster time scale optimization. This solves the problem that single-time-scale scheduling cannot adequately consider the response differences of multiple processes, achieving robust and safe scheduling that adapts to the response differences of coal mine power supply, heating, and coal transportation while resisting uncertain disturbances. Furthermore, during the current optimization period at each time scale, this invention dynamically corrects the safety constraint boundaries of the physical quantities corresponding to that time scale based on the characteristics of the uncertain disturbances at that time scale. By dynamically correcting the constraint boundaries rather than reserving fixed static margins, it can specifically resist specific uncertain disturbances at that scale, proactively mitigating the risks to coal mine safety caused by raw coal mining, outdoor temperature fluctuations, and electrical load fluctuations. It effectively improves the safety and robustness of system operation with only a small economic cost, achieving a better balance between economy and safety. Attached Figure Description

[0031] Figure 1 This is a schematic flowchart of the robust safety scheduling method for the electricity-heat-coal layered rolling system in a coal mine energy system according to an embodiment of the present invention.

[0032] Figure 2 This is a schematic diagram of the timing structure of the multi-timescale hierarchical rolling optimization scheduling framework according to an embodiment of the present invention.

[0033] Figure 3 This is a graph showing the change in coal storage at the bottom of the auxiliary shaft according to an embodiment of the present invention.

[0034] Figure 4 This is a real-time temperature change curve in the employee dormitory according to an embodiment of the present invention.

[0035] Figure 5 This is a real-time temperature change curve inside the main wellbore according to an embodiment of the present invention.

[0036] Figure 6 This is a time-series variation curve of the minimum voltage at a distribution network node according to an embodiment of the present invention. Detailed Implementation

[0037] The invention will now be further described with reference to the accompanying drawings.

[0038] Example 1:

[0039] like Figure 1As shown, this embodiment provides a robust safety scheduling method for a coal mine energy system with stratified rolling operation of electricity, heat, and coal. This method aims to address the problem that single-time-scale scheduling cannot simultaneously accommodate differences in multi-process responses and the safety of uncertain disturbances. By binding multi-time-scale stratification with dynamic safety margin correction, robust safety scheduling that adapts to both response differences and resists uncertain disturbances is achieved. This method mainly includes the following three core steps:

[0040] Step S100: Based on the differences in response time of various physical processes of electricity, heat and coal in the coal mine energy system, a multi-timescale hierarchical rolling optimization framework is constructed. The physical processes with different response speeds correspond to different rolling optimization levels at different time scales, and the optimization results at the slow time scale are used as boundary conditions or reference trajectories for optimization at the fast time scale.

[0041] Specifically, coal mine energy systems contain various heterogeneous energy flows with fundamentally different physical response characteristics. Coal transportation involves material handling and storage, possessing energy storage and buffering characteristics, with response times typically on the order of hours. Heating transmission relies on heat transfer media and pipelines, and is affected by transmission delays and thermal inertia, resulting in response times typically on the order of minutes. Electricity transmission is completed almost instantaneously, lacking physical buffering, with response times on the order of seconds or even shorter. Using a single time scale for scheduling inevitably leads to a dilemma: if the time scale is too long, it cannot capture transient fluctuations in fast processes like electricity, easily causing safety accidents such as voltage exceeding limits; if the time scale is too short, it cannot cover the complete dynamic cycle of slow processes like coal transportation, causing optimization decisions to be limited to the current time period and resulting in short-sighted behavior. Therefore, it is necessary to bind response time differences with hierarchical structures and construct a multi-time-scale hierarchical rolling optimization framework. The optimization results at the slow time scale serve as boundary conditions or reference trajectories for the fast time scale optimization. Their physical significance lies in the following: the slow process changes dynamically at a slow pace, and once its scheduling plan is formulated, it should be strictly enforced as a rigid constraint (fixed boundary) in the fast-scale optimization to prevent the fast-scale optimization from undermining the safety and stability of the slow process in pursuit of local economy. At the same time, the fast process changes dynamically at a rapid pace, and the plan issued at the slow scale can only serve as a flexible guide (reference trajectory) for it. The fast scale needs to make corrections near this trajectory based on real-time fluctuations, thereby achieving a combination of rigidity and flexibility in interlayer coupling.

[0042] Step S200: During the current optimization period at each time scale, dynamically correct the physical quantity safety constraint boundary corresponding to that time scale based on the uncertainty disturbance characteristics of that time scale.

[0043] Specifically, the nature of disturbances faced at different time scales is drastically different. Slow time scales primarily face cumulative disturbances, such as persistent deviations in raw coal mining output. These deviations accumulate over time, potentially leading to both storage overflows and idling of transport equipment. Medium time scales primarily face environmental disturbances, such as prediction errors in outdoor temperature. These errors, transmitted through thermal inertia, could lead to both failure of shaft antifreeze systems and overheating waste. Fast time scales primarily face impulsive disturbances, such as sudden load increases on high-power equipment like coal mining machines. These disturbances manifest as unidirectional power surges, easily causing unidirectional voltage drops. Traditional static margin reservation methods cannot adapt to the differences in the nature of these disturbances: facing cumulative disturbances, fixed margins may be exhausted due to the continuous accumulation of deviations; facing impulsive disturbances, fixed margins may become completely ineffective due to incorrect reservation direction. Therefore, margin adjustments must be made dynamically based on the characteristics of uncertain disturbances. The "dynamic correction" referred to in this embodiment means that in each rolling optimization period, the physical quantity safety constraint boundary is calculated and tightened in real time based on the predicted upper limit of disturbance deviation or the upper limit of sudden increase in the current period, thereby reserving dynamic adjustment space for actual operating deviations.

[0044] Step S300: Based on the multi-timescale hierarchical rolling optimization framework and the dynamically corrected physical quantity safety constraint boundary, a scheduling plan for the coal mine energy system is generated.

[0045] Specifically, within the hierarchical framework constructed in step S100, each time-scale level performs rolling optimization within the safety constraint boundaries dynamically corrected in step S200. The optimization solver, while satisfying the dynamically tightened boundary constraints, generates scheduling plans for each level with the goal of achieving optimal coordination between system operation economy and safety. For example, a long-term timescale generates a coal transportation plan and preliminary power and heating plans; a short-term timescale generates a final heating plan and a revised power supply plan; and a real-time dynamic timescale generates a final power output plan. This scheduling method, combining a hierarchical framework with dynamic margin correction, ensures that the scheduling at each time scale not only matches the physical response speed but also specifically resists certain uncertainties at that scale, achieving robust and safe scheduling that adapts to response differences and resists uncertainties.

[0046] It should be noted that the above-mentioned rolling optimization solution process is based on a multi-network operation model of the coal mine energy system. Specifically, the coal transportation network model describes the unidirectional flow of coal from the coal mining face through the auxiliary shaft coal bunker, the main shaft coal bunker, and to the surface coal bunker, as well as the dynamic balance constraints of the storage capacity of each coal bunker; the heating network operation model describes the thermal balance constraints between heat source output, pipeline transmission delay, and heat load; and the power flow calculation model of the distribution network describes the active and reactive power balance and voltage distribution constraints of each node. These three types of network models together constitute the underlying constraints of the rolling optimization at each time scale. Under the premise of satisfying the above-mentioned network operation constraints and the dynamically corrected safety constraint boundaries, the optimization solver generates a feasible scheduling plan.

[0047] Furthermore, such as Figure 2 As shown, the multi-timescale hierarchical rolling optimization framework includes a long-timescale rolling optimization level, a short-timescale rolling optimization level, and a real-time dynamic-scale rolling optimization level. Slow-response physical processes correspond to the long-timescale rolling optimization level, medium-response physical processes correspond to the short-timescale rolling optimization level, and fast-response physical processes correspond to the real-time dynamic-scale rolling optimization level. The scheduling plan generated by the long-timescale rolling optimization level serves as the fixed boundary between the short-timescale and real-time dynamic-scale rolling optimization levels, while the scheduling plan generated by the short-timescale rolling optimization level serves as the reference trajectory for the real-time dynamic-scale rolling optimization level. This differentiated coupling and transmission logic is the core of this invention: the slow-timescale plan serves as a fixed boundary because the slow-response physical process changes dynamically and slowly. Its scheduling plan should be strictly enforced as a rigid constraint in the fast-scale optimization to prevent the fast-scale optimization from arbitrarily changing the operation arrangement of the slow process in pursuit of local economy. The medium-timescale plan serves as a reference trajectory because although the medium-response physical process has a certain inertia, it still needs to be appropriately corrected based on updated prediction information. Therefore, the guidance for the faster scale should be a flexible reference trajectory, allowing the real-time dynamic scale to fluctuate and correct near the trajectory, achieving a combination of rigidity and flexibility in inter-layer coupling. As a specific implementation method, the slow-response physical process is a coal transportation process with energy storage buffer characteristics, the medium-response physical process is a heat transmission process with thermal inertia, and the fast-response physical process is a power transmission process with instantaneous balance characteristics.

[0048] Furthermore, based on Example 1, the corresponding logic between the uncertainty disturbance characteristics and the physical quantity safety constraint boundary under long-term scales, as well as the dynamic correction method of the buffer capacity boundary, are elaborated in detail.

[0049] First, the correspondence between the characteristics of uncertainty disturbances and the physical quantity safety constraint boundary includes: the cumulative disturbances in raw coal mining and the coal bunker storage boundary on a long time scale. Specifically, the long time scale mainly faces cumulative disturbances, which are not instantaneous shocks but deviations that accumulate over time. In coal mine energy systems, there is often a continuous deviation between the predicted and actual values ​​of raw coal production. This deviation has significant two-way characteristics: when the actual output exceeds the predicted value, excess coal continuously flows into the coal bunker, accumulating over time, which can easily lead to safety accidents such as coal bunker overflow; when the actual output is lower than the predicted value, the outflow of coal continuously exceeds the inflow, and the storage capacity is continuously consumed over time, which can easily lead to empty coal bunkers or even equipment damage risks such as belt conveyor idling. Precisely because cumulative disturbances have this two-way risk of causing storage overflow or conveyor idling, the corresponding physical quantity safety constraint boundary must be the coal bunker storage boundary with energy storage buffer characteristics.

[0050] Furthermore, during the current optimization period on a long-term timescale, the maximum deviation of coal storage capacity is calculated based on the upper limit of the cumulative disturbance from raw coal mining, and the upper and lower limits of coal storage capacity are tightened based on this maximum deviation. The upper limit of the cumulative disturbance from raw coal mining is the upper limit of the predicted deviation from the fully mechanized mining face output. The maximum deviation of the coal storage capacity The calculation formula is:

[0051]

[0052] In the formula, To be fed into the secondary coal bunker The collection of fully mechanized mining faces The unit dispatch interval is on a long-term timescale; the tightened upper and lower limits of coal storage capacity are:

[0053]

[0054] In the formula, for auxiliary coal bunker Coal reserves; , These are the secondary coal bunkers. The maximum and minimum capacity.

[0055] As mentioned earlier, raw coal mining deviations are bidirectional; overproduction will cause storage levels to deviate upwards until overflowing, while underproduction will cause storage levels to deviate downwards until empty. Therefore, it is necessary to calculate the maximum deviation in coal storage levels. At the same time, the upper and lower limits of storage capacity will be tightened, that is, within the original maximum capacity limit. Subtract from the basis At the original minimum capacity limit Add to the basis This allows for symmetrical adjustment space to accommodate cumulative deviations in both directions. If only a unidirectional margin is used, for example, only the upper limit constraint is tightened (i.e., only the subtraction of...). Maintaining the lower limit (while keeping it unchanged) can only defend against the risk of overproduction leading to overflow; when actual underproduction occurs, the storage volume may still fall below the original minimum lower limit, resulting in empty storage exceeding the limit, at which point the unilateral reserve becomes completely ineffective. Conversely, tightening only the lower limit constraint cannot defend against the risk of overproduction leading to overflow. Therefore, two-way tightening is not a simple mathematical symmetry, but rather a profound match to the physical nature of cumulative disturbances exceeding the limit in both directions, and is an inevitable choice to ensure that the buffer capacity does not exceed the limit in any deviation direction. This embodiment uses this dynamic calculation based on deviation from the upper limit. The mechanism of tightening the upper and lower limits in both directions enables the rolling optimization over a long time scale to proactively reserve dynamic adjustment space for the cumulative deviations in raw coal mining, effectively buffering the adverse effects of fluctuations in raw coal production and deviations in transportation power, and significantly enhancing the coal transportation system's ability to resist uncertain disturbances.

[0056] Furthermore, based on Example 1, the corresponding logic between the uncertainty disturbance characteristics and the physical quantity safety constraint boundary under a short time scale, as well as the dynamic correction method of the temperature operating boundary, are elaborated in detail.

[0057] First, the correspondence between the characteristics of uncertainty disturbances and the safety constraints of physical quantities includes: the outdoor temperature environment disturbances corresponding to short time scales and the operating boundary of shaft antifreeze temperature. Short time scales mainly face environmental disturbances, which originate from forecast errors in meteorological conditions. In coal mine energy systems, actual outdoor temperatures often deviate from rolling forecasts. This deviation directly affects the air temperature inside the shaft through heat conduction via the building envelope or soil. Since shaft antifreeze is a crucial aspect of ensuring safe mine production, the existence of outdoor temperature deviations brings significant two-way risks: when the actual outdoor temperature is lower than the forecast, the cold air intrusion effect intensifies, potentially leading to excessively low temperatures inside the shaft, resulting in antifreeze failure or even safety accidents such as icing of hoisting equipment; when the actual outdoor temperature is higher than the forecast, heat load demand decreases. If the heating system continues to operate at the originally predicted high load, it may lead to excessively high temperatures inside the shaft, causing not only serious energy waste but also potentially affecting the comfort of the underground working environment. Because environmental disturbances pose a dual risk of causing antifreeze failure or overheating waste, the corresponding physical quantity safety constraint boundary must be the wellbore antifreeze temperature operating boundary with thermal inertia constraint characteristics.

[0058] Furthermore, within the current optimization period on a short timescale, the upper limit of the wellbore antifreeze temperature deviation is calculated based on the upper limit of the deviation of the outdoor temperature environment disturbance, and the upper and lower limit constraints of the wellbore antifreeze temperature are tightened based on this upper limit. Here, the upper limit of the outdoor temperature environment disturbance is the upper limit of the deviation of the actual outdoor temperature relative to the rolling forecast value. The wellbore antifreeze temperature deviates from the upper limit. The calculation formula is:

[0059]

[0060] In the formula, The soil heat transfer coefficient, For wellbore heat transfer area The scheduling interval is a short-time unit. The specific heat capacity of air, air density, For wellbore The air volume; the upper and lower limits of the tightened wellbore antifreeze temperature are:

[0061]

[0062] In the formula, for Time well The temperature of the internal air, wellbore The upper and lower limits of the internal air temperature.

[0063] First, regarding The derivation mechanism is as follows: the outdoor temperature deviation is not directly equivalent to the temperature deviation inside the well, but needs to be determined through the soil heat transfer coefficient. Heat transfer area Physical parameters are converted. This is because there is thermal resistance between the well casing and the outdoor environment; the outdoor temperature deviation must undergo heat transfer through the soil or enclosure structure before it manifests as a temperature deviation in the air inside the well casing. The denominator in the formula... This represents the heat capacity of the air inside the wellbore, reflecting the air's ability to absorb or release heat, leading to temperature changes. Therefore, The calculation essentially quantifies the maximum temperature deviation that accumulates in the wellbore air heat capacity after the most unfavorable outdoor temperature deviation passes through the heat transfer path. This derivation process profoundly matches the thermal inertia physical nature of the heat transfer process.

[0064] Secondly, regarding the necessity of two-way tightening: as mentioned earlier, the risks posed by outdoor temperature deviations are two-way; too low a temperature leads to antifreeze failure, while too high a temperature results in energy waste. Therefore, it is necessary to calculate the deviation of the wellbore's antifreeze temperature from the upper limit. At the same time, the upper and lower limits of temperature are tightened, that is, the original upper temperature limit is tightened. Subtract from the basis At the original lower temperature limit Add to the basis This allows for symmetrical adjustment space to accommodate bidirectional environmental disturbances. If only unidirectional tightening is used, for example, only tightening the lower limit constraint (i.e., only adding...) Maintaining the upper limit unchanged only defends against the risk of antifreeze failure caused by low outdoor temperatures. When the actual outdoor temperature is higher, the internal temperature of the well casing may still exceed the original upper limit constraint, leading to overheating and energy waste, at which point the unidirectional reserve margin becomes completely ineffective. Conversely, tightening only the upper limit constraint cannot defend against the safety risk of antifreeze failure. Therefore, bidirectional tightening is not a simple mathematical symmetry, but a profound match to the physical nature of bidirectional exceedance of limits by environmental disturbances, and is an inevitable choice to ensure that the target space temperature does not exceed the limit under any deviation direction. This embodiment uses this dynamic calculation based on deviation from the upper limit. The mechanism of tightening the upper and lower limits in both directions enables rolling optimization on a short time scale to proactively reserve dynamic adjustment space for outdoor temperature deviations, effectively improving the heating system's ability to cope with uncertain disturbances and reducing the risk of indoor temperature fluctuations.

[0065] Furthermore, based on Example 1, the corresponding logic between the uncertainty disturbance characteristics and the physical quantity safety constraint boundary under the real-time dynamic scale, as well as the dynamic correction method of the voltage operating range boundary, are elaborated in detail.

[0066] First, the correspondence between the characteristics of uncertain disturbances and the physical quantity safety constraint boundaries includes: the impact load disturbances corresponding to the real-time dynamic scale and the voltage operating range boundaries of the distribution network nodes. Specifically, the real-time dynamic scale mainly faces impact disturbances, which originate from the frequent start-up and shutdown or operating condition switching of high-power equipment, manifested as drastic power fluctuations within a very short time. In coal mine energy systems, heavy equipment such as coal mining machines and hoists exhibit pulsed load characteristics when operating, and their power deviation manifests as a unidirectional upward mutation of the baseline value, i.e., a power surge. This impact load surge disrupts the original power balance of the distribution network. Due to the instantaneous balance characteristics of power transmission and the lack of physical buffers, the power surge will rapidly lead to a rapid drop in voltage at the feeder end node, triggering a unidirectional voltage drop risk. Precisely because impact disturbances have this specific risk of causing a unidirectional voltage drop, the corresponding physical quantity safety constraint boundary must be the voltage operating range boundary of the distribution network nodes, reflecting the instantaneous power balance state.

[0067] Furthermore, during the current optimization period at a real-time dynamic scale, the maximum voltage drop is calculated based on the upper limit of the power surge caused by the impulsive electrical load disturbance and the voltage sensitivity matrix. The lower limit constraint of the distribution network node voltage is then tightened based on the maximum voltage drop. The voltage sensitivity matrix includes the sensitivity coefficients of the node voltage amplitude to the active power injected into the node. and the sensitivity coefficient to reactive power injection at nodes The formula for calculating the voltage deviation caused by node power deviation is:

[0068]

[0069] In the formula, Let be the voltage deviation at node i. and These represent the injected active and reactive power deviations at node j, respectively; the upper limit of the power surge of the sudden charge disturbance includes the upper limit of the active power surge of the impulsive load. and the upper limit of sudden increase in reactive power The maximum voltage drop The calculation formula is:

[0070]

[0071] In the formula, The set of nodes connected to impulsive loads; the tightened lower limit constraint for distribution network node voltage is:

[0072]

[0073] In the formula, for Time Node voltage, They are nodes The lower and upper limits of the allowable voltage.

[0074] First, regarding the physical meaning of sensitivity matrix extraction: voltage sensitivity matrix and Essentially, these are elements of the inverse of the Jacobian matrix in the power flow equations of the distribution network; they quantify the power injection changes at any node j in the network. and ) for the voltage amplitude of critical node i ( The linear impact of a sudden surge in load power on the entire grid is determined by extracting this matrix. This invention decouples the complex nonlinear power flow mapping relationship into an intuitive linear sensitivity coefficient, enabling rapid and accurate assessment of the most adverse impact of such a surge in load power on the overall grid voltage within a very short optimization window on a real-time dynamic scale.

[0075] Secondly, regarding the maximum voltage drop... The calculation logic is as follows: Since the impact load deviation manifests as a unidirectional abrupt change upward from the baseline value, that is, a sudden increase in active and reactive power ( and This unidirectional surge inevitably leads to a unidirectional voltage drop at nodes in most radial distribution network topologies. To quantify the depth of this voltage drop under the most unfavorable operating condition, the absolute value of the sensitivity coefficient is used in the formula. and This is then multiplied by the upper limit of the power surge and summed. Absolute values ​​are used instead of algebraic values ​​because when considering the worst-case safety boundary, it must be assumed that the surge effects of all impulsive loads superimpose and point in the direction of the voltage drop, thus calculating the maximum possible voltage drop. .

[0076] Finally, regarding the necessity of unidirectional tightening of the lower limit rather than bidirectional tightening: this is determined by the unidirectional physical nature of impulsive disturbances. As mentioned earlier, sudden increases in impulsive loads mainly cause the risk of voltage drops, rarely leading to significant upward voltage exceedances. Therefore, when revising the voltage operating range boundaries, it is only necessary to adjust within the original allowable lower voltage limit. Add to the basis This allows for a tightening of the lower limit constraint in advance, providing a one-way safety margin for voltage dips; while the upper limit of the voltage allowable range... Then it remains unchanged. If, in this scenario, a two-way tightening logic similar to that used for coal bunker storage or shaft antifreeze temperature is still mechanically applied, that is, simultaneously subtracting... Tightening the upper limit not only fails to defend against actual voltage dips but also unnecessarily compresses the legal space for upward voltage fluctuations, leading to overly conservative dispatch plans. It might even misjudge a slight voltage rise caused by a normal load reduction as exceeding the limit, forcing power supply equipment to perform unnecessary reactive power reversal adjustments, resulting in energy waste and misallocation of control resources. Therefore, unidirectional tightening of the lower limit, rather than bidirectional tightening, is not a simple mathematical trade-off but a profound match to the physical nature of unidirectional voltage dips caused by impulsive disturbances. It is an inevitable choice to ensure that the distribution network can withstand the risk of sudden voltage drops without sacrificing economy and control flexibility. This embodiment uses dynamic calculation based on sensitivity matrix and power surge upper limit... The mechanism of unidirectionally tightening the lower limit allows the rolling optimization of real-time dynamic scale to reserve adjustment space for the system in advance, effectively improving the voltage support capability of the distribution network and reducing the risk of node voltage exceeding the limit.

[0077] This embodiment also provides an electronic device, which may be a dedicated server, industrial control computer, or virtual computing node deployed in a coal mine energy dispatch center, or a cloud computing platform. The electronic device includes a processor and a memory. When the processor executes the computer program stored in the memory, it implements the steps of the coal mine energy system's electricity-heat-coal stratified rolling robust safety dispatching method as described in Embodiment 1.

[0078] Furthermore, in order to map the core features in the aforementioned method to specific hardware execution logic, the electronic device includes a multi-timescale hierarchical rolling optimization module and a dynamic safety margin correction module.

[0079] The multi-timescale hierarchical rolling optimization module is configured to construct a multi-timescale hierarchical rolling optimization framework based on the response time differences of various physical processes of electricity, heat, and coal in the coal mine energy system, and generate a scheduling plan for the coal mine energy system based on the multi-timescale hierarchical rolling optimization framework and the dynamically corrected physical quantity safety constraint boundary.

[0080] The dynamic safety margin correction module is configured to dynamically correct the physical quantity safety constraint boundary corresponding to each time scale during the current optimization period, based on the uncertainty disturbance characteristics of that time scale.

[0081] Furthermore, this embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the robust safety scheduling method for the electricity-heat-coal stratified rolling mechanism in a coal mine energy system as described in Embodiment 1.

[0082] Example 2:

[0083] A multi-timescale rolling optimization scheduling framework based on MPC (Multi-Period Calculation) is constructed, ranging from 30 min to 15 min to 5 min. Through rolling updates of multi-scale strategies in the prediction domain and system state feedback correction, hierarchical coordinated adaptive scheduling of electricity, heat, and coal under multi-dimensional uncertainty is achieved. Secondly, based on multi-layer rolling optimization, to address disturbances caused by prediction errors during actual operation, a robust scheduling model considering dynamic safety margins is constructed, focusing on the first time interval within each prediction domain (i.e., the control domain). This model dynamically corrects the bottom coal storage boundary, heating temperature constraints, and node voltage adjustment ranges based on sensitivity matrices, proactively mitigating the safety risks to coal mine operations caused by raw coal mining, outdoor temperature fluctuations, and electrical load variations. Finally, by calling the solver to solve the constructed optimization model, a coal transportation plan, heating equipment operation plan, and power supply equipment operation plan that balance economy and safety are generated, improving the system's operational robustness.

[0084] In this embodiment, to verify the superiority of the proposed robust rolling scheduling method for coal mine energy systems that takes into account dynamic safety margins, two scenarios are set up for comparative analysis.

[0085] Scenario 1: Multi-timescale rolling optimization without considering the robust scheduling model of the control domain.

[0086] Scenario 2: Consider multi-timescale rolling optimization of a robust scheduling model in the control domain.

[0087] The time-varying information required for the rolling optimization stage, such as raw coal mining volume, outdoor temperature, renewable energy output, and electrical load, is obtained using a rolling prediction method. The prediction results can be generated by methods such as neural networks and grey prediction. To enhance the universality of the case study analysis, this invention introduces random perturbations based on the rolling prediction results to characterize the uncertainties on both the source and load sides during actual operation. The software used to perform the case simulation is MATLAB_R2021a with the YALMIP toolbox configured, using the GUROBI solver. The simulation platform used has an Intel Core i5-9300H processor, 24GB of memory, and a 64-bit Windows 11 operating system.

[0088] Figure 2 The temporal structure of the constructed 30min-15min-5min multi-timescale rolling optimization scheduling framework is presented. As shown in the figure, the 30min rolling layer performs rolling optimization over the remaining scheduling cycle within the day, with its prediction domain gradually shrinking over time. The 15min rolling layer further expands within the 30min layer control domain, using a fixed 2-hour prediction domain that rolls and shifts over time to continuously correct power supply and heating plans. The 5min real-time dynamic adjustment layer targets the 15min layer control domain, dynamically correcting the purchased power and generator output based on real-time operational information. This scheduling architecture can meet the differentiated dynamic adjustment needs of coal transportation, heating, and power supply, and continuously incorporate the latest operational information through rolling corrections, thereby improving the flexibility, adaptability, and operational reliability of multi-network collaborative scheduling.

[0089] Table 1 shows the actual operating results in different scenarios. The actual operating results differ significantly between the two scenarios with and without the introduction of the control domain robust scheduling model. Compared to Scenario 1, Scenario 2, after considering the dynamic safety margin, saw an increase of 195.14 yuan in energy purchase cost, an increase of approximately 0.22%; while the network loss penalty cost decreased by approximately 10.34%; and the voltage deviation rate also decreased from 2.37% to 2.29%, a decrease of 0.08 percentage points. It can be seen that although the system energy purchase cost increases slightly after introducing the control domain robust scheduling model, the network loss penalty cost and voltage deviation level are significantly improved. This indicates that the proposed strategy can effectively improve the safety and robustness of system operation with only a small economic cost. This is because considering the dynamic safety margin allows the scheduling process to reserve a certain adjustment space for the system in advance, thereby mitigating the risk of the distribution network operating state deviating from the safe range when facing source load fluctuations and operational disturbances, and enhancing the system's ability to withstand uncertainties. In summary, the proposed multi-timescale robust scheduling strategy that considers dynamic safety margins can achieve a better trade-off between economy and security, demonstrating good application value.

[0090] Table 1 Actual running results in different scenarios

[0091]

[0092] like Figure 3 The diagram illustrates the real-time coal storage changes in the auxiliary shaft bottom coal bunker under scenarios 1 and 2. In scenario 1, due to the discrepancy between raw coal production forecasts and actual operations, the coal storage in the bunker falls below the safety lower limit between 3:45 and 5:15, indicating that the coal transportation plan is not highly adaptable to fluctuations in raw coal production during this period, easily leading to the risk of belt conveyor idling. In contrast, scenario 2, based on multi-timescale rolling optimization, further considers dynamic safety margins, reserving necessary adjustment space for bunker operation. This effectively buffers the adverse effects of raw coal production fluctuations and transportation power deviations, ensuring that the coal storage in the auxiliary shaft bottom coal bunker remains within safe constraints. Therefore, the proposed robust scheduling strategy significantly enhances the coal transportation system's resilience to uncertain disturbances, avoids transportation anomalies caused by exceeding bunker storage limits, and plays a positive role in improving the safety and reliability of system operation.

[0093] Real-time changes in indoor temperature of employee dormitories under scenarios 1 and 2, such as Figure 4As shown in the figure, in Scenario 1, due to uncertainties such as outdoor temperature fluctuations, the actual indoor temperature repeatedly fell below the comfort lower limit of 18℃, indicating that the heating dispatching in this scenario has a weak ability to adapt to external disturbances and cannot continuously guarantee users' comfort needs. In contrast, Scenario 2 considers a safety margin during the dispatching process, effectively constraining indoor temperature deviations. As can be seen from the figure, the actual indoor temperature in the employee dormitory in Scenario 2 was consistently maintained within the comfort range of 18℃ to 22℃, without any temperature exceeding the limit. This demonstrates that the proposed robust dispatching strategy can effectively improve the heating system's ability to cope with uncertain disturbances and reduce the risk of indoor temperature fluctuations.

[0094] Real-time temperature changes inside the main shaft under scenarios 1 and 2, such as Figure 5 As shown in the diagram, in Scenario 1, under the influence of random fluctuations in outdoor temperature, the actual temperature of the shaft repeatedly fell below the 2°C safety threshold for freezing, indicating that conventional rolling optimization has certain limitations in dealing with external low-temperature disturbances and cannot fully guarantee the safe operation of the coal mine shaft against freezing. Simultaneously, temperatures were also higher than normal at certain times, reflecting a certain redundancy in heating regulation and potential energy waste. In contrast, Scenario 2 further considered dynamic safety margins during the scheduling process, effectively suppressing shaft temperature fluctuations. The actual temperature of the main shaft remained within the specified safety range, and no temperature exceedances occurred. This demonstrates that the proposed robust scheduling strategy considering dynamic safety margins can effectively improve the shaft heating system's ability to withstand uncertain disturbances, improving the economic efficiency of heating scheduling while effectively ensuring shaft freezing safety.

[0095] The real-time changes in the minimum voltage of coal mine distribution network nodes under scenarios 1 and 2, such as... Figure 6 As shown, during the 00:00-07:00 period, due to the lack of reactive power support from photovoltaic power generation equipment and frequent fluctuations in impact loads such as mining and hoisting, the system operation faces strong uncertainties and disturbances. Scenario 1 does not consider safety margins, thus its adaptability to the above disturbances is limited, resulting in the minimum node voltage repeatedly falling below the safety lower limit of 0.95 pu, adversely affecting the voltage stability and power supply security of the coal mine distribution network. In contrast, Scenario 2 reserves a voltage safety margin in the scheduling model and adjusts and compensates in advance by coordinating adjustable resources within the system, ensuring that the minimum node voltage is always maintained above 0.95 pu. Therefore, the proposed robust scheduling strategy can effectively improve the voltage support capability of the coal mine distribution network, reduce the risk of node voltage exceeding limits, and has significant advantages in ensuring the safe and stable operation of the distribution network.

Claims

1. A coal mine energy system electric-thermal-coal layered rolling robust safety scheduling method, characterized in that, Based on the response time differences of various physical processes of electricity, heat, and coal in the coal mine energy system, a multi-timescale hierarchical rolling optimization framework is constructed. Physical processes with different response speeds correspond to different rolling optimization levels at different time scales, and the optimization results at the slow time scale are used as boundary conditions or reference trajectories for optimization at the fast time scale. During the current optimization period at each time scale, the safety constraint boundaries of the bottom coal bunker, heating temperature, and node voltage at that time scale are dynamically corrected based on the uncertainty disturbance characteristics corresponding to that time scale. Based on the multi-timescale hierarchical rolling optimization framework and the dynamically corrected safety constraints of the coal bunker at the bottom of the mine, heating temperature, and node voltage, a scheduling plan for the coal mine energy system is generated.

2. The robust safety scheduling method for the electricity-heat-coal stratified rolling operation of a coal mine energy system according to claim 1, characterized in that, The multi-timescale hierarchical rolling optimization framework includes a long-timescale rolling optimization hierarchy, a short-timescale rolling optimization hierarchy, and a real-time dynamic-scale rolling optimization hierarchy. Slow-response physical processes correspond to the long-time-scale rolling optimization level, medium-response physical processes correspond to the short-time-scale rolling optimization level, and fast-response physical processes correspond to the real-time dynamic-scale rolling optimization level. The coal transportation plan generated by the long-term rolling optimization level serves as the fixed boundary between the short-term rolling optimization level and the real-time dynamic rolling optimization level, while the generated power supply and heating operation plans serve as the reference trajectories for the short-term rolling optimization level and the real-time dynamic rolling optimization level. The heating operation plan generated by the short-timescale rolling optimization level serves as the fixed boundary of the real-time dynamic scale rolling optimization level, and the generated power supply operation plan serves as the reference trajectory of the real-time dynamic scale rolling optimization level.

3. The robust safety scheduling method for the electricity-heat-coal layered rolling system of a coal mine energy system according to claim 2, characterized in that, The slow-response physical process is a coal transportation process with energy storage and buffering characteristics; the medium-response physical process is a heat transmission process with thermal inertia; and the fast-response physical process is a power transmission process with instantaneous balance characteristics.

4. The robust safety scheduling method for the electricity-heat-coal stratified rolling operation of a coal mine energy system according to claim 1, characterized in that, The correspondence between the uncertainty disturbance characteristics and the physical quantity safety constraint boundaries includes: the cumulative disturbance of raw coal mining and the coal storage boundary corresponding to the long time scale; the outdoor temperature environment disturbance and the shaft antifreeze temperature operation boundary corresponding to the short time scale; and the impact electrical load disturbance and the distribution network node voltage operation range boundary corresponding to the real-time dynamic scale.

5. The robust safety scheduling method for the electricity-heat-coal stratified rolling operation of a coal mine energy system according to claim 4, characterized in that, During the current optimization period on the aforementioned long-term timescale, the maximum deviation value of coal storage is calculated based on the upper limit of the cumulative disturbance of raw coal mining, and the upper and lower limits of coal storage are tightened based on the maximum deviation value; the upper limit of the cumulative disturbance of raw coal mining is the upper limit of the predicted deviation of the fully mechanized mining face output. The maximum deviation of the coal storage capacity The calculation formula is: In the formula, To be fed into the secondary coal bunker The collection of fully mechanized mining faces The unit dispatch interval is on a long-term timescale; the tightened upper and lower limits of coal storage capacity are: In the formula, for auxiliary coal bunker Coal reserves; , These are the secondary coal bunkers. The maximum and minimum capacity.

6. The robust safety scheduling method for the electricity-heat-coal stratified rolling operation of a coal mine energy system according to claim 4, characterized in that, During the current optimization period on the short time scale, the upper limit deviation of the wellbore antifreeze temperature is calculated based on the upper limit deviation of the outdoor temperature environment disturbance, and the upper and lower limit constraints of the wellbore antifreeze temperature are tightened based on the upper limit deviation of the wellbore antifreeze temperature. The upper limit of the deviation of the outdoor temperature environment disturbance is the upper limit of the deviation of the actual outdoor temperature relative to the rolling forecast value. The wellbore antifreeze temperature deviates from the upper limit. The calculation formula is: In the formula, The soil heat transfer coefficient, Let w be the heat transfer area of ​​the wellbore. The scheduling interval is a short-time unit. The specific heat capacity of air, air density, Let w be the air volume of the wellbore; the upper and lower limits of the wellbore's antifreeze temperature after tightening are: In the formula, for Time well shaft The temperature of the internal air, , wellbore The upper and lower limits of the internal air temperature.

7. The robust safety scheduling method for the electricity-heat-coal layered rolling system in a coal mine energy system according to claim 4, characterized in that, During the current optimization period at the real-time dynamic scale, the maximum voltage drop is calculated based on the upper limit of the power surge of the impulsive electrical load disturbance and the voltage sensitivity matrix, and the lower limit constraint of the distribution network node voltage is tightened based on the maximum voltage drop. The voltage sensitivity matrix includes the sensitivity coefficients of node voltage amplitude to the injected active power at the node. and the sensitivity coefficient to reactive power injection at nodes The formula for calculating the voltage deviation caused by node power deviation is: In the formula, Let be the voltage deviation at node i. and These represent the injected active and reactive power deviations at node j, respectively; the upper limit of the power surge of the sudden charge disturbance includes the upper limit of the active power surge of the impulsive load. and the upper limit of sudden increase in reactive power The maximum voltage drop The calculation formula is: In the formula, The set of nodes connected to impulsive loads; the tightened lower limit constraint for distribution network node voltage is: In the formula, for Time Node voltage, They are nodes The lower and upper limits of the allowable voltage.

8. An electronic device comprising a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the steps of the robust and safe scheduling method for the electricity-heat-coal stratification of a coal mine energy system as described in claim 1.

9. The electronic device according to claim 8, wherein, The electronic device includes a multi-timescale hierarchical rolling optimization module and a dynamic safety margin correction module. The multi-timescale hierarchical rolling optimization module is configured to construct a multi-timescale hierarchical rolling optimization framework based on the response time differences of various physical processes of electricity, heat, and coal in the coal mine energy system, and generate a scheduling plan for the coal mine energy system based on the multi-timescale hierarchical rolling optimization framework and the dynamically corrected physical quantity safety constraint boundary. The dynamic safety margin correction module is configured to dynamically correct the physical quantity safety constraint boundary corresponding to each time scale during the current optimization period, based on the uncertainty disturbance characteristics of that time scale.

10. A storage medium storing a computer program, which, when executed by a processor, implements the steps of the robust and safe scheduling method for the electricity-heat-coal stratification of a coal mine energy system as described in claim 1.

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