An optimization scheduling method and system for coal preparation power supply system

Abstract

This invention discloses an optimized scheduling method for a coal preparation power supply system, comprising the following steps: acquiring power flow and material flow parameters and importing and defining decision variables; establishing a multi-equipment, multi-condition power consumption model for the coal preparation plant, and combining the coupling relationship of power flow-coal flow-water flow-medium flow to establish a two-layer low-carbon optimized scheduling model for the coal preparation plant power supply system; importing the day-ahead production plan and the day-ahead wind and solar power forecast output, and initializing the time period parameter t=1; calling a linear solver to solve the upper-level model, solving for the coal bunker inventory, the transport capacity of the belt conveyor between coal bunkers, the production volume of the workshop, and the scheduling plan of the power flow; using the particle swarm optimization algorithm to solve the lower-level model, solving for the operating speed of the front-end transport equipment of the processing equipment; exporting the solution results of the lower-level model and feeding them back to the upper-level model, updating the production capacity of each workshop, and determining whether the time period is greater than or equal to 96. If it is, the final scheduling scheme is generated; otherwise, the above steps are repeated until the time period condition is met.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and in particular to an optimized dispatching method and system for a coal preparation power supply system. Background Technology

[0002] Among numerous coal preparation plants, whether they are coking coal preparation plants focused on coking coal production, thermal coal preparation plants serving the demand for thermal coal, or coal preparation plants employing different processes such as wet and dry methods, all face multiple challenges under the dual carbon targets, including technological upgrades, stricter environmental standards, and intensified market competition. As a crucial indicator of a coal preparation plant's competitiveness, reducing operating costs and improving operational efficiency have become paramount research priorities.

[0003] Existing methods for reducing operating costs mostly focus on optimal sorting density, raw coal blending, industrial engineering management, and start-up / shutdown sequence, with less emphasis on coordinating tidal flow and material flow to reduce energy, water, and media costs in coal preparation plants. Summary of the Invention

[0004] This solution addresses the problems and needs raised above by proposing an optimized scheduling method and system for coal preparation power supply. Due to the adoption of the following technical features, it can achieve the above-mentioned technical objectives and bring about several other technical benefits.

[0005] One objective of this invention is to provide an optimized scheduling method for a coal preparation power supply system, comprising the following steps:

[0006] S10: Based on the topology of the given coal preparation plant and the flow direction distribution of each material flow, obtain and import the tidal flow and material flow parameters, and define relevant decision variables;

[0007] S20: Establish a power consumption model for multiple equipment and multiple operating conditions in a coal preparation plant that considers coal flow operation. Combine the coupling relationship between power flow, coal flow, water flow, and medium flow to establish a two-layer low-carbon optimization scheduling model for the power supply system of a coal preparation plant based on multi-flow coordination. The two-layer low-carbon optimization scheduling model includes an upper-layer model and a lower-layer model.

[0008] S30: Import the day-ahead production plan and the day-ahead wind and solar power forecast output, and initialize the time period parameter t=1. When 32≤t≤36 or 47≤t≤53, use 1min as the scheduling time interval; otherwise, use 15min as the scheduling time interval.

[0009] S40: Call the linear solver to solve the upper-level model, and solve for the coal bunker inventory, the transport capacity of the belt conveyor between coal bunkers, the production volume of the workshop, and the scheduling plan of the power flow.

[0010] S50: The particle swarm optimization algorithm is used to solve the lower-level model to determine the operating speed of the front-end transportation equipment of the processing equipment;

[0011] S60: Export the solution results of the lower-level model and feed them back to the upper-level model, update the production capacity of each workshop, and determine whether the time period is greater than or equal to 96. If it is, generate the final scheduling plan; otherwise, repeat steps S40 to S60 until the time period condition is met.

[0012] In addition, the optimized scheduling method for the coal preparation power supply system according to the present invention may also have the following technical features:

[0013] In one example of the present invention, in step S20, the power consumption model of a coal preparation plant with multiple equipment and multiple operating conditions considering coal flow operation includes:

[0014]

[0015] in, These are the time sets of the equipment under the following conditions: shutdown, startup, fluctuating operation, rated operation, and overload operation; W x,y,t Let y be the average processing volume of device y in device x during the time period t. Let y be the power factor of device y in device x when it is operating under no-load conditions; The power factor of the equipment under startup conditions; These are the starting operating power, fluctuating operating power, rated operating power, and overload operating power of device y in device x. This describes the relationship between power consumption and processing capacity under equipment overload conditions. This represents the rated processing capacity of the equipment during the t-th time period.

[0016] In one example of the present invention, in step S20, the coupling relationship between the tidal flow, coal flow, water flow, and medium flow includes:

[0017] The amount of coal flow processed determines the power consumption of the corresponding processing equipment; the amount of coal flow processed determines the amount of medium flow used; within a certain range, the amount of water flow used affects the amount of medium loss; the amount of medium flow and water flow used determines the power consumption of the corresponding processing system.

[0018] In one example of the present invention, the amount of coal flow processed determines the amount of medium flow used, including:

[0019] The amount of coal flow processed determines the amount of media flow used, including:

[0020] Coal preparation plant media usage Q M,t The relationship between the flow rate of coal processed by the heavy medium cyclone during time period t is expressed as follows:

[0021]

[0022] In the formula, Q M,t W DMC,t These represent the amount of medium used and the throughput of the heavy medium cyclone in time period t, respectively. ρ M These represent the amount of heavy medium suspension required per unit of material and the density of the heavy medium suspension, respectively.

[0023] In one example of the present invention, the expression for the water flow usage is:

[0024]

[0025] In the formula, Q W,t , These represent the water demand during time period t, the water volume used for media preparation, the water volume used for media removal, and the water volume used for other auxiliary systems. For the water recycling system's recovery efficiency, W DMC,t The throughput of the heavy medium cyclone during the time period t; ρ M These represent the amount of heavy medium suspension required per unit of material and the density of the heavy medium suspension, respectively.

[0026] In one example of the present invention, within a certain range, the impact of water flow usage on medium loss includes: the relationship between medium loss and water flow usage is expressed as follows:

[0027]

[0028] In the formula, K2 and K3 represent the relationship between the amount of media loss and the amount of media used, and the impact of the water consumption of the dewatering equipment on media recovery, respectively; Q W,t Let t be the water demand during the time period t. Let t be the amount of water used for demediation during the time period t.

[0029] In one example of the present invention, the amount of medium flow and water flow used determines the power consumption of the corresponding processing system, including:

[0030] The relationship between the usage of the medium flow and water flow and the power consumption of the treatment system is expressed as follows:

[0031]

[0032] In the formula, P MR&C,t P W,t These represent the average power consumption of the media recovery and circulation system and the water circulation system during time period t, respectively; k MR&C k W These are the relationship coefficients between the amount of medium and water used and the power values ​​of the medium recovery and circulation system and the water circulation system, respectively. These are the operating coefficients for the media recovery and circulation system and the water circulation system, respectively; Q W,t , These represent the water demand during time period t, the water volume used for media preparation, the water volume used for media removal, and the water volume used for other auxiliary systems.

[0033] In one example of the present invention, in step S20, the upper-level model aims to minimize the energy cost F1 per ton of refined coal and the medium and water consumption cost F2 per ton of refined coal:

[0034] minF a =F1+F2

[0035] in,

[0036]

[0037] In the formula, F a The objective function of the upper-level model; C represents the average power purchased from the large power grid during the t-th time period; p,t C is the electricity purchase price from the grid per unit time period; WT and δ WT,t These are the operation and maintenance costs and wind curtailment costs for wind power installations, respectively; C PV and δ PV,t These represent the operation and maintenance costs and curtailment costs of photovoltaic power generation, respectively; P WT,t and P PV,t Let be the average power output of wind power and solar power respectively during hour t; and Q W,t C represents the loss of the medium and the water demand during time period t, respectively; M,t and C W,t C represents the average unit cost of the medium and water resources during time period t, respectively. CO2,t W represents the carbon emission cost during time period t. Bc,1,t The amount of coal to be prepared for the raw coal preparation workshop; These represent the total carbon emissions of the coal preparation plant, the natural escape emissions from raw coal, the indirect emissions from purchased electricity, and the indirect emissions from wastewater treatment, respectively, within the time period t. These are, respectively, the methane emission coefficient after unit coal mining, the comprehensive carbon emission coefficient of purchased electricity, and the maximum methane production capacity per unit of wastewater treatment; These represent the global warming coefficient of methane and the methane concentration, respectively. ω is the methane correction factor; ct For carbon price;

[0038] The lower-level model uses the average processing volume W of device y (numbered y among devices x) during the time period t.x,y,t With rated processing capacity The objective is to minimize the deviation between them.

[0039]

[0040] In the formula, F b This is the objective function of the upper-level model.

[0041] In one example of the present invention, the dual-layer low-carbon optimization scheduling model for the power supply system of a coal preparation plant based on multi-flow coordination should also satisfy power flow constraints and coal flow constraints.

[0042] The power flow constraints include power flow constraints of the distribution network simulated using a linearized DistFlow formula, as well as node voltage constraints and upper and lower limits of output of distributed generation sources.

[0043] The node voltage constraint and the upper and lower limits of the distributed power generation output are expressed as follows:

[0044]

[0045] In the formula, U i,t Let U be the voltage at node i in hour t. min U max These represent the lower and upper limits of the node voltage, respectively; P WT,max P PV,max These are the maximum output values ​​for wind power and solar power, respectively.

[0046] The coal flow constraints should meet the constraints of equipment processing capacity, transportation links, and storage links.

[0047] The device processing capacity constraint is expressed as follows:

[0048]

[0049] Where, This represents the maximum processing capacity of the device in time period t.

[0050] The storage constraint is expressed as follows:

[0051]

[0052] Where, For time period t, x s The amount of coal in the type of storage equipment, where x s ∈Ω storage Ω storage A collection of devices for storage. For the rated capacity of the storage device, Let t be the amount of material stored in the storage device during time period t. Let t be the amount of material transported out of the storage device during time period t.

[0053] Another objective of this invention is to provide an optimized scheduling system for a coal preparation power supply system, comprising:

[0054] The parameter acquisition unit is configured to acquire and import tidal flow and material flow parameters based on the topology of a given coal preparation plant and the flow direction distribution of each material flow, and to define relevant decision variables.

[0055] The scheduling model unit is configured to establish a multi-equipment, multi-condition power consumption model for a coal preparation plant that considers coal flow operation. It combines the coupling relationship between power flow, coal flow, water flow, and medium flow to establish a two-layer low-carbon optimization scheduling model for the power supply system of the coal preparation plant based on multi-flow coordination. The two-layer low-carbon optimization scheduling model includes an upper-layer model and a lower-layer model.

[0056] The data import unit is configured to import the day-ahead production plan and the day-ahead wind and solar power forecast output, and initialize the time period parameter t=1. When 32≤t≤36 or 47≤t≤53, the scheduling time interval is 1 minute; otherwise, the scheduling time interval is 15 minutes.

[0057] The upper-level model solving unit is configured to call the linear solver to solve the upper-level model, and to solve the coal bunker inventory, the transport capacity of the belt conveyor between coal bunkers, the production volume of the workshop, and the scheduling plan of the power flow.

[0058] The lower-level model solving unit is configured to use the particle swarm optimization algorithm to solve the lower-level model and determine the operating speed of the front-end transportation equipment of the processing equipment.

[0059] The scheme generation unit is configured to export the solution results of the lower-level model and feed them back to the upper-level model, update the production capacity of each workshop, and determine whether the time period is greater than or equal to 96. If it is, the final scheduling scheme is generated; otherwise, steps S40 to S60 are executed repeatedly until the time period condition is met.

[0060] The present invention has the following advantages over the prior art:

[0061] This invention establishes a multi-equipment, multi-condition power consumption model for coal preparation plants that considers coal flow operation. It comprehensively considers the constraints of the coupling relationship between tidal flow and coal flow, medium flow, and water flow, the virtual energy storage function of the coal bunker, the start-up and shutdown sequence of each piece of equipment, and the regulating effect of the transportation system on coal flow. This model guides coal preparation plants to achieve multi-flow coordinated and optimized operation, reduces the energy cost per ton of clean coal and the medium and water consumption costs per ton of clean coal, improves the operating efficiency of coal preparation plants, and reduces the carbon emissions of coal preparation plants, resulting in significant economic and environmental benefits.

[0062] The preferred embodiments of the invention will be described in more detail below with reference to the accompanying drawings, so as to facilitate an understanding of the features and advantages of the invention. Attached Figure Description

[0063] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0064] Figure 1 This is a diagram illustrating the power supply system architecture of a coal preparation plant based on multi-flow coupling, according to an embodiment of the present invention.

[0065] Figure 2 This is a flowchart of an optimized scheduling method for a coal preparation plant power supply system based on multi-flow collaboration, according to an embodiment of the present invention.

[0066] Figure 3 This is a schematic diagram of an optimized scheduling strategy for a coal preparation plant power supply system based on multi-flow coordination, according to an embodiment of the present invention.

[0067] Figure 4 This is a diagram showing the operating conditions of the main equipment under Case 1 and Case 2 in time periods according to an embodiment of the present invention;

[0068] Figure 5 This is a diagram showing power consumption in different scenarios according to embodiments of the present invention;

[0069] Figure 6 This diagram illustrates the system's externally purchased electricity and carbon emissions under different scenarios according to embodiments of the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0071] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0072] According to a first aspect of the present invention, an optimized scheduling method for a coal preparation power supply system is provided, such as... Figure 1 , Figure 2 and Figure 3 As shown, it includes the following steps:

[0073] S10: Based on the topology of the given coal preparation plant and the flow direction distribution of each material flow, obtain and import the tidal flow and material flow parameters, and define relevant decision variables;

[0074] S20: Establish a power consumption model for multiple equipment and multiple operating conditions in a coal preparation plant that considers coal flow operation. Combine the coupling relationship between power flow, coal flow, water flow, and medium flow to establish a two-layer low-carbon optimization scheduling model for the power supply system of a coal preparation plant based on multi-flow coordination. The two-layer low-carbon optimization scheduling model includes an upper-layer model and a lower-layer model.

[0075] S30: Import the day-ahead production plan and the day-ahead wind and solar power forecast output, and initialize the time period parameter t=1. When 32≤t≤36 or 47≤t≤53, use 1min as the scheduling time interval; otherwise, use 15min as the scheduling time interval.

[0076] S40: Call the linear solver to solve the upper-level model, and solve for the coal bunker inventory, the transport capacity of the belt conveyor between coal bunkers, the production volume of the workshop, and the scheduling plan of the power flow; for example, the linear solver is the YALMIP+Gurobi 11.0.3 solver;

[0077] S50: The particle swarm optimization algorithm is used to solve the lower-level model to determine the operating speed of the front-end transportation equipment of the processing equipment;

[0078] S60: Export the solution results of the lower-level model and feed them back to the upper-level model, update the production capacity of each workshop, and determine whether the time period is greater than or equal to 96. If it is, generate the final scheduling plan; otherwise, repeat steps S40 to S60 until the time period condition is met.

[0079] This method establishes a multi-equipment, multi-condition power consumption model for coal preparation plants that considers coal flow operation. It comprehensively considers the constraints of the coupling relationship between tidal flow and coal flow, medium flow, and water flow, the virtual energy storage function of the coal bunker, the start-up and shutdown sequence of each piece of equipment, and the regulating effect of the transportation system on coal flow. This guides coal preparation plants to achieve multi-flow coordinated and optimized operation, reduces the energy cost per ton of clean coal and the medium and water consumption cost per ton of clean coal, improves the operating efficiency of coal preparation plants, and reduces the carbon emissions of coal preparation plants, resulting in significant economic and environmental benefits.

[0080] In one example of the present invention, in step S20, the power consumption model of a coal preparation plant with multiple equipment and multiple operating conditions considering coal flow operation includes:

[0081]

[0082] in, These are the time sets of the equipment under the following conditions: shutdown, startup, fluctuating operation, rated operation, and overload operation; W x,y,t Let y be the average processing capacity of device y in device x during the time period t, in tons; Let y be the power factor of device y in device x when it is operating under no-load conditions; The power factor of the equipment under startup conditions; These are the starting operating power, fluctuating operating power, rated operating power, and overload operating power of device y in device x, respectively, in kW; This describes the relationship between power consumption and processing capacity under equipment overload conditions. This represents the rated processing capacity of the equipment during the t-th time period, expressed in tons.

[0083] In addition to the electricity consumption of the main coal preparation equipment, the coal preparation plant also has lighting costs. LS,t Office area P OFFICE,t Air compressor P AC,t Other auxiliary systems P auxiliary,t The total electricity consumption of the coal preparation plant, P total,t for:

[0084]

[0085] In one example of the present invention, in step S20, the coupling relationship between the tidal flow, coal flow, water flow, and medium flow includes:

[0086] The amount of coal flow processed determines the power consumption of the corresponding processing equipment; the amount of coal flow processed determines the amount of medium flow used; within a certain range, the amount of water flow used affects the amount of medium loss; the amount of medium flow and water flow used determines the power consumption of the corresponding processing system.

[0087] In one example of the present invention, the amount of coal flow processed determines the amount of medium flow used, including:

[0088] The amount of coal flow processed determines the amount of media flow used, including:

[0089] Coal preparation plant media usage Q M,t The relationship between the flow rate of coal processed by the heavy medium cyclone during time period t is expressed as follows:

[0090]

[0091] In the formula, Q M,t W DMC,t These represent the amount of medium used and the throughput of the heavy medium cyclone in time period t, respectively, both in tons; ρ M These represent the amount of heavy medium suspension required per unit of material and the density of the heavy medium suspension, respectively.

[0092] In one example of the present invention, the expression for the water flow usage is:

[0093]

[0094] In the formula, Q W,t , These represent the water demand during time period t, the water volume used for media preparation, the water volume used for media removal, and the water volume used for other auxiliary systems, all in tons. For the water recycling system's recovery efficiency, W DMC,t The throughput of the heavy medium cyclone during the time period t; ρ M These represent the amount of heavy medium suspension required per unit of material and the density of the heavy medium suspension, respectively.

[0095] In one example of the present invention, within a certain range, the impact of water flow usage on medium loss includes: the relationship between medium loss and water flow usage is expressed as follows:

[0096]

[0097] In the formula, K2 and K3 represent the relationship between the amount of media loss and the amount of media used, and the impact of the water consumption of the dewatering equipment on media recovery, respectively; Q W,t Let t be the water demand during the time period t. Let t be the amount of water used for demediation during the time period t.

[0098] Specifically, coal preparation plants are typically equipped with media recovery and circulation systems, but due to limitations in demediation efficiency, the media cannot be 100% recovered. Therefore, it can be approximated that, assuming the separation process and equipment technology remain unchanged, the amount of media loss... The amount of medium used is directly proportional. Within a certain range. As the water consumption of desliming equipment (desliming screens and magnetic separators) increases, the media loss decreases accordingly. The media loss in a coal preparation plant can be expressed by the formula above.

[0099] Due to limitations of the relevant equipment, the desliming equipment has a maximum water consumption limit:

[0100]

[0101] Where, The maximum water consumption for the desliming equipment is expressed in tons.

[0102] In one example of the present invention, the amount of medium flow and water flow used determines the power consumption of the corresponding processing system, including:

[0103] The relationship between the usage of the medium flow and water flow and the power consumption of the treatment system is expressed as follows:

[0104]

[0105] In the formula, P MR&C,t P W,t These represent the average power consumption of the media recovery and circulation system and the water circulation system during time period t, respectively; k MR&C k W These are the relationship coefficients between the amount of medium and water used and the power values ​​of the medium recovery and circulation system and the water circulation system, respectively. These are the operating coefficients for the media recovery and circulation system and the water circulation system, respectively; Q W,t , These represent the water demand during time period t, the water volume used for media preparation, the water volume used for media removal, and the water volume used for other auxiliary systems.

[0106] In one example of the present invention, in step S20, the upper-level model aims to minimize the energy cost F1 per ton of refined coal and the medium and water consumption cost F2 per ton of refined coal:

[0107] in,

[0108]

[0109] In the formula, F aThe objective function of the upper-level model; C represents the average power purchased from the power grid during the t-th time period, in kW. p,t The electricity price purchased from the grid per unit time period is expressed in yuan / kWh; C WT and δ WT,t These represent the operation and maintenance costs and wind curtailment costs of wind power configurations, respectively, in yuan / kWh; C PV and δ PV,t These represent the operation and maintenance costs and curtailment costs of photovoltaic power generation, respectively, in yuan / kWh; P WT,t and P PV,t These are the average power outputs of wind power and solar power respectively within hour t, in kWh; and Q W,t C represents the loss of the medium and the water demand during time period t, respectively, in tons; M,t and C W,t These are the average unit costs of the medium and water resources during the time period t, respectively, in yuan / ton; W represents the carbon emission cost during time period t, expressed in yuan. Bc,1,t The amount of coal entering the raw coal preparation workshop, in tons; These represent the total carbon emissions from the coal preparation plant, the natural escape emissions from raw coal, the indirect emissions from purchased electricity, and the indirect emissions from wastewater treatment, respectively, within the time period t, in tons. These are, respectively, the methane emission coefficient after unit coal mining, the comprehensive carbon emission coefficient of purchased electricity, and the maximum methane production capacity per unit of wastewater treatment; These represent the global warming coefficient of methane and the methane concentration, respectively. ω is the methane correction factor; ct For carbon price;

[0110] The lower-level model uses the average processing volume W of device y (numbered y among devices x) during the time period t. x,y,t With rated processing capacity The objective is to minimize the deviation between them.

[0111]

[0112] In the formula, F b This is the objective function of the upper-level model.

[0113] In one example of the present invention, the dual-layer low-carbon optimization scheduling model for the power supply system of a coal preparation plant based on multi-flow coordination should also satisfy power flow constraints and coal flow constraints.

[0114] The power flow constraints include power flow constraints of the distribution network simulated using a linearized DistFlow formula, as well as node voltage constraints and upper and lower limits of output of distributed generation sources.

[0115] The node voltage constraint and the upper and lower limits of the distributed power generation output are expressed as follows:

[0116]

[0117] In the formula, U i,t Let U be the voltage at node i in hour t. min U max These represent the lower and upper limits of the node voltage, respectively; P WT,max P PV,max These are the maximum output values ​​for wind power and solar power, respectively.

[0118] The coal flow constraints should meet the constraints of equipment processing capacity, transportation links, and storage links.

[0119] The device processing capacity constraint is expressed as follows:

[0120]

[0121] In the formula, This represents the maximum processing capacity of the equipment in time period t, in tons.

[0122] The storage constraint is expressed as follows:

[0123]

[0124] In the formula, For time period t, x s The amount of coal in the type of storage equipment, where x s ∈Ω storage Ω storage A collection of devices for storage. For the rated capacity of the storage device, Let t be the amount of material stored in the storage device during time period t. The amount of material transported out of the storage equipment during time period t is expressed in tons.

[0125] An optimized scheduling system for a coal preparation power supply system according to a second aspect of the present invention includes:

[0126] The parameter acquisition unit is configured to acquire and import tidal flow and material flow parameters based on the topology of a given coal preparation plant and the flow direction distribution of each material flow, and to define relevant decision variables.

[0127] The scheduling model unit is configured to establish a multi-equipment, multi-condition power consumption model for a coal preparation plant that considers coal flow operation. It combines the coupling relationship between power flow, coal flow, water flow, and medium flow to establish a two-layer low-carbon optimization scheduling model for the power supply system of the coal preparation plant based on multi-flow coordination. The two-layer low-carbon optimization scheduling model includes an upper-layer model and a lower-layer model.

[0128] The data import unit is configured to import the day-ahead production plan and the day-ahead wind and solar power forecast output, and initialize the time period parameter t=1. When 32≤t≤36 or 47≤t≤53, the scheduling time interval is 1 minute; otherwise, the scheduling time interval is 15 minutes.

[0129] The upper-level model solving unit is configured to call the linear solver to solve the upper-level model, and to solve the coal bunker inventory, the transport capacity of the belt conveyor between coal bunkers, the production volume of the workshop, and the scheduling plan of the power flow.

[0130] The lower-level model solving unit is configured to use the particle swarm optimization algorithm to solve the lower-level model and determine the operating speed of the front-end transportation equipment of the processing equipment.

[0131] The scheme generation unit is configured to export the solution results of the lower-level model and feed them back to the upper-level model, update the production capacity of each workshop, and determine whether the time period is greater than or equal to 96. If it is, the final scheduling scheme is generated; otherwise, steps S40 to S60 are executed repeatedly until the time period condition is met.

[0132] This system establishes a multi-equipment, multi-condition power consumption model for coal preparation plants that considers coal flow operation. It comprehensively considers the constraints of the coupling relationship between tidal flow and coal flow, medium flow, and water flow, the virtual energy storage function of the coal bunker, the start-up and shutdown sequence of each piece of equipment, and the regulating effect of the transportation system on coal flow. This guides the coal preparation plant to achieve multi-flow coordinated and optimized operation, reduces the energy cost per ton of clean coal and the medium and water consumption cost per ton of clean coal, improves the operating efficiency of the coal preparation plant, and reduces the carbon emissions of the coal preparation plant, resulting in significant economic and environmental benefits.

[0133] The scheduling mode of the two-layer low-carbon optimization scheduling method for the power supply system of a coal preparation plant based on multi-flow coordination is as follows: Figure 3 As shown, the scheduling method is as follows:

[0134] Optimize the operating speed of the belt conveyor to match the processing capacity of each piece of equipment, thereby improving the production efficiency of the coal preparation plant; fully leverage the virtual energy storage characteristics of the coal bunker in terms of demand response and the regulating role of the belt conveyor on coal flow, and reduce the overall energy consumption level based on the time-of-use electricity pricing mechanism while meeting the daily production plan; optimize the start-up and shutdown sequence of each piece of equipment to accelerate the start-up speed of the coal preparation plant and reduce the energy consumption level during the start-up phase; and solve for the optimal solution based on the coupling relationship between medium flow and water flow to further reduce the overall level of medium and water consumption.

[0135] Specific Cases

[0136] To verify the effectiveness of the proposed two-layer low-carbon optimization scheduling method for the power supply system of a coal preparation plant based on multi-flow collaboration, four different scenarios were set up, and simulation verification was carried out using a coal preparation plant in Anhui Province as an example.

[0137] Case 1: The coordination between tidal flow and coal flow is not considered; the coordination between coal flow, medium flow, and water flow is not considered; intelligent start-stop is not considered.

[0138] Case 2 considers the coordination of tidal flow and coal flow; it does not consider the coordination of coal flow-medium flow-water flow; it does not consider intelligent start-stop.

[0139] Case 3: Consider the coordination of tidal flow, coal flow, medium flow, and water flow; do not consider intelligent start-stop.

[0140] Case 4: Consider the coordination of tidal flow, coal flow, medium flow, and water flow; consider intelligent start-stop.

[0141] The upper-level model is transformed into a mixed-integer linear programming model after linearization, and the problem is solved by calling the YALMIP+Gurobi 11.0.3 solver. The lower-level model is solved using the particle swarm optimization algorithm.

[0142] Taking into account the working cycle of each device, a 15-minute time interval was selected, with a scheduling cycle of 24 hours and a total of 96 time periods. Among them, within the time periods 32≤t≤36 and 47≤t≤53, a 1-minute time interval was used, and intelligent start-stop was implemented.

[0143] The specific process of intelligent start-stop is as follows:

[0144] The system intelligently assesses the current production conditions and rationally schedules the start-up and shutdown sequence of equipment. During startup, equipment related to the adjustment of the qualified medium liquid density is started first. Once the qualified medium liquid density stabilizes and meets the standard, other equipment is started sequentially with the coal flow. During shutdown, the system intelligently determines the coal-free time points for each piece of equipment in the coal flow system and shuts down each piece of equipment immediately upon the absence of coal, following the direction of coal flow. The following time constraints exist when using this equipment startup scheme:

[0145] Before starting the coal feeder in the start-up bin, start the belt conveyor under the coal feeder. The belt conveyor starts at [t]. Bc,y Restart the coal feeder after a few seconds, and so on, gradually starting the next level of production equipment until all equipment on the production line is started. When shutting down, allow a safety margin at the point where there is no coal to be produced before stopping.

[0146]

[0147] in, The time it takes for materials to be transported by a belt conveyor to reach the next level equipment, expressed in hours (h). These are the coal-free time points for each piece of equipment in the coal flow system; This refers to the equipment's shutdown time point; For safety margin, the unit is h;

[0148] Simulation verification results:

[0149] Operating conditions of major equipment during period 16 are as follows Figure 4 As shown. Thanks to the regulating effect of the coal flow, which matches the processing capacity of each piece of equipment, under time period 16, Case 2, compared to Case 1, not only avoids the equipment from operating under overload conditions and causing a large amount of loss, but also makes the equipment operate under rated conditions as much as possible, thereby increasing the output per unit time period.

[0150] Power consumption in four scenarios as follows Figure 5 As shown in Case 2, the coordination of coal flow and power flow allows for appropriate reduction of production load and lower energy consumption during peak electricity price periods, while still meeting the daily production plan. During off-peak electricity price periods, the regulating effect of coal flow enables the coal preparation plant to operate at full load, resulting in higher power output compared to Case 1.

[0151] System purchased electricity and carbon emissions in different scenarios, such as Figure 6 As shown, the application of coal flow regulation of tidal flow and intelligent start-stop further reduced the carbon emission level of the system. Compared with Case 1, the carbon emission per ton of clean coal in Case 4 was reduced by 16.31%.

[0152] The results of optimized operation of the coal preparation plant under four scenarios are shown in Table 1. Compared with Case 1, the energy cost per ton of clean coal in Case 3 decreased by 12.67%, and the overall cost of medium and water consumption per ton of clean coal decreased by 3.45%. Due to the adoption of the intelligent start-stop scheme, compared with Case 3, the output of clean coal in Case 4 increased by 1.51%, and the power consumption per ton of clean coal decreased by 1.44%.

[0153] In summary, the proposed optimization scheduling method and system for a coal preparation power supply system in this embodiment includes: Step 1: Based on the given topology of the coal preparation plant and the flow direction distribution of each material flow, obtain and import the power flow and material flow parameters, and define relevant decision variables; Step 2: Establish a multi-equipment, multi-condition power consumption model for the coal preparation plant considering coal flow operation, and combine the coupling constraints of power flow-coal flow-water flow-medium flow to establish a two-layer low-carbon optimization scheduling model for the coal preparation plant power supply system based on multi-flow coordination; Step 3: Import the day-ahead production plan and the day-ahead wind and solar power forecast output, and initialize the time period parameter t=1; Step 4: Call YALMIP+Gurobi Step 11.0.3: The solver solves the upper-level model, determining the main coal bunker inventory, the transport capacity of the belt conveyor between coal bunkers, the production volume of the main workshops, and the scheduling plan for power flow. Step 5: The particle swarm optimization algorithm is used to solve the lower-level model, determining the operating speed of the front-end transport equipment of the main processing equipment. Step 6: The solution results of the lower-level model are exported and fed back to the upper level to update the production capacity of each workshop. It is then determined whether the time period is greater than or equal to 96. If it is, the final scheduling plan is generated; otherwise, steps 4 to 6 are executed repeatedly. This invention can effectively reduce the energy cost per ton of clean coal and the media and water consumption costs per ton of clean coal in coal preparation plants, improve the operating efficiency of coal preparation plants, and reduce carbon emissions, resulting in significant economic and environmental benefits.

[0154] Table 1 Comparison of results under different scenarios

[0155]

[0156] The foregoing description, with reference to preferred embodiments, details an exemplary implementation of the optimized scheduling method and system for a coal preparation power supply system proposed by the present invention. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of the present invention, and various combinations can be made to the various technical features and structures proposed by the present invention without exceeding the protection scope of the present invention, the protection scope of the present invention being determined by the appended claims.

Claims

1. An optimized scheduling method for a coal preparation power supply system, characterized in that, The steps include: S10: Based on the topology of the given coal preparation plant and the flow direction distribution of each material flow, obtain and import the tidal flow and material flow parameters, and define relevant decision variables; S20: Establish a multi-equipment, multi-condition power consumption model for a coal preparation plant considering coal flow operation. Combining the coupling relationship between power flow, coal flow, water flow, and medium flow, establish a two-layer low-carbon optimization scheduling model for the coal preparation plant's power supply system based on multi-flow coordination. This two-layer low-carbon optimization scheduling model includes an upper-layer model and a lower-layer model. The multi-equipment, multi-condition power consumption model for a coal preparation plant considering coal flow operation includes: in, , , , , These are the time sets of the equipment under the following conditions: shutdown, startup, fluctuating operation, rated operation, and overload operation. for x The equipment is numbered as y The device in the t Average processing volume over a given period; Let y be the power factor of device y in device x when it is operating under no-load conditions; The power factor of the equipment under startup conditions; , , , These are the starting operating power, fluctuating operating power, rated operating power, and overload operating power of device y in device x. This describes the relationship between power consumption and processing capacity under equipment overload conditions. This represents the rated processing capacity of the equipment during the t-th time period. S30: Import the day-ahead production plan and the day-ahead wind and solar power forecast output, and initialize the time period parameter t=1. When 32≤t≤36 or 47≤t≤53, use 1min as the scheduling time interval; otherwise, use 15min as the scheduling time interval. S40: Call the linear solver to solve the upper-level model, and solve for the coal bunker inventory, the transport capacity of the belt conveyor between coal bunkers, the production volume of the workshop, and the scheduling plan of the power flow. S50: The particle swarm optimization algorithm is used to solve the lower-level model to determine the operating speed of the front-end transportation equipment of the processing equipment; S60: Export the solution results of the lower-level model and feed them back to the upper-level model, update the production capacity of each workshop, and determine whether the time period is greater than or equal to 96. If it is, generate the final scheduling plan; otherwise, repeat steps S40 to S60 until the time period condition is met.

2. The optimized scheduling method for the coal preparation power supply system according to claim 1, characterized in that, In step S20, the coupling relationship between the tidal flow, coal flow, water flow, and medium flow includes: The amount of coal flow processed determines the power consumption of the corresponding processing equipment; the amount of coal flow processed determines the amount of medium flow used; within a certain range, the amount of water flow used affects the amount of medium loss; the amount of medium flow and water flow used determines the power consumption of the corresponding processing system.

3. The optimized scheduling method for the coal preparation power supply system according to claim 2, characterized in that, The amount of coal flow processed determines the amount of media flow used, including: Media usage in coal preparation plants With the t The relationship between the coal flow rate processed by the heavy medium cyclone within a time period is expressed as follows: Where, , The amount of medium used and the heavy medium cyclone in the first stage are respectively t Processing volume within a time period; , These represent the amount of heavy medium suspension required per unit of material and the density of the heavy medium suspension, respectively.

4. The optimized scheduling method for the coal preparation power supply system according to claim 2, characterized in that, The expression for the amount of water used is: Where, , , , These represent the water demand during time period t, the water volume used for media preparation, the water volume used for media removal, and the water volume used for other auxiliary systems, respectively. To improve the recycling efficiency of the water circulation system, For heavy medium cyclones in the first t Processing volume within a time period; , These represent the amount of heavy medium suspension required per unit of material and the density of the heavy medium suspension, respectively.

5. The optimized scheduling method for the coal preparation power supply system according to claim 2, characterized in that, Within a certain range, the amount of water used affects the amount of medium loss in the following ways: Medium loss The relationship between water usage and flow rate is expressed as follows: Where, , The relationship between media loss and media usage, and the impact of water consumption in the demediation equipment on media recovery are respectively characterized. This represents the amount of media used during the t-th time period; The amount of water used for desliming during the t-th time period; This is the upper limit for the amount of water used for demediation.

6. The optimized scheduling method for the coal preparation power supply system according to claim 2, characterized in that, The amount of medium flow and water flow used determines the power consumption of the corresponding processing system, including: The relationship between the usage of the medium flow and water flow and the power consumption of the treatment system is expressed as follows: Where, , These are the average power consumption of the media recovery and circulation system and the water circulation system during the time period t, respectively. , These are the relationship coefficients between the amount of medium and water used and the power values ​​of the medium recovery and circulation system and the water circulation system, respectively. , These are the operating conditions of the media recovery and circulation system and the water circulation system, respectively. This represents the amount of media used during the t-th time period; , , These represent the water volume used for media preparation, the water volume used for media removal, and the water volume used for other auxiliary systems during the time period t, respectively.

7. The optimized scheduling method for the coal preparation power supply system according to claim 2, characterized in that, In step S20, the upper-level model uses the energy cost per ton of clean coal. And the cost of medium and water consumption per ton of refined coal Minimum as the target: in, Where, The objective function of the upper-level model; For the first t The average power purchased from the main power grid during the time period; The electricity price purchased from the grid per unit time period; and These are the operation and maintenance costs of wind power and the cost of wind curtailment, respectively. and These are the operation and maintenance costs and curtailment costs of photovoltaic power generation, respectively. and The first t Average output of wind and solar power within one hour; and The first t The amount of medium loss and water demand over a period of time; and The first t The average unit cost of media and water resources over a given time period; For the first t Carbon emission costs over a given period of time; The amount of coal to be prepared for the raw coal preparation workshop; , , , The first t The total carbon emissions of the coal preparation plant, natural escape emissions from raw coal, indirect emissions from purchased electricity, and indirect emissions from wastewater treatment within the specified time period. quantity; , , These are, respectively, the methane emission coefficient after unit coal mining, the comprehensive carbon emission coefficient of purchased electricity, and the maximum methane production capacity per unit of wastewater treatment; , These represent the global warming coefficient of methane and the methane concentration, respectively. This is the methane correction factor; For carbon price; The lower-level model is x The equipment is numbered as y The device in the t Average processing volume over the time period With rated processing capacity The objective is to minimize the deviation between them. Where, This is the objective function of the upper-level model.

8. The optimized scheduling method for the coal preparation power supply system according to claim 2, characterized in that, The dual-layer low-carbon optimization scheduling model for the power supply system of a coal preparation plant based on multi-flow coordination should also satisfy power flow constraints and coal flow constraints. The power flow constraints include power flow constraints of the distribution network simulated using a linearized DistFlow formula, as well as node voltage constraints and upper and lower limits of output of distributed generation sources. The node voltage constraint and the upper and lower limits of the distributed power generation output are expressed as follows: Where, For the node at hour t voltage, , These are the lower and upper limits of the node voltage, respectively. , These are the maximum output values ​​for wind power and solar power, respectively. The coal flow constraints should meet the constraints of equipment processing capacity, transportation links, and storage links. The device processing capacity constraint is expressed as follows: Where, This represents the maximum processing capacity of the device in time period t. The storage constraint is expressed as follows: Where, For the first t Time The amount of coal in the type of storage equipment, of which... , A collection of devices for storage. For the rated capacity of the storage device, For the first t The amount of material stored in the storage device during a given time period. For the first t The amount of material transported out of the storage equipment during a given period.

9. A scheduling system employing the optimized scheduling method for the coal preparation power supply system as described in claim 1, characterized in that, include: The parameter acquisition unit is configured to acquire and import tidal flow and material flow parameters based on the topology of a given coal preparation plant and the flow direction distribution of each material flow, and to define relevant decision variables. The scheduling model unit is configured to establish a multi-equipment, multi-condition power consumption model for a coal preparation plant that considers coal flow operation. Combining the coupling relationship between power flow, coal flow, water flow, and medium flow, a two-layer low-carbon optimization scheduling model for the coal preparation plant's power supply system based on multi-flow coordination is established. This two-layer low-carbon optimization scheduling model includes an upper-layer model and a lower-layer model. The multi-equipment, multi-condition power consumption model for a coal preparation plant considering coal flow operation includes: in, , , , , These are the time sets of the equipment under the following conditions: shutdown, startup, fluctuating operation, rated operation, and overload operation. for x The equipment is numbered as y The device in the t Average processing volume over a given period; Let y be the power factor of device y in device x when it is operating under no-load conditions; The power factor of the equipment under startup conditions; , , , These are the starting operating power, fluctuating operating power, rated operating power, and overload operating power of device y in device x. This describes the relationship between power consumption and processing capacity under equipment overload conditions. This represents the rated processing capacity of the equipment during the t-th time period. The data import unit is configured to import the day-ahead production plan and the day-ahead wind and solar power forecast output, and initialize the time period parameter t=1. When 32≤t≤36 or 47≤t≤53, the scheduling time interval is 1 minute; otherwise, the scheduling time interval is 15 minutes. The upper-level model solving unit is configured to call the linear solver to solve the upper-level model, and to solve the coal bunker inventory, the transport capacity of the belt conveyor between coal bunkers, the production volume of the workshop, and the scheduling plan of the power flow. The lower-level model solving unit is configured to use the particle swarm optimization algorithm to solve the lower-level model and determine the operating speed of the front-end transportation equipment of the processing equipment. The scheme generation unit is configured to export the solution results of the lower-level model and feed them back to the upper-level model, update the production capacity of each workshop, and determine whether the time period is greater than or equal to 96. If it is, the final scheduling scheme is generated; otherwise, steps S40 to S60 are executed repeatedly until the time period condition is met.

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