Load distribution method and device and readable storage medium

Abstract

The invention provides a load distribution method and device, a readable storage medium, and an electronic device, and the electronic device creates a constraint condition, determines an objective function and an optimization direction, and carries out the load distribution of an electric facility according to the objective function under the constraint of the constraint condition and the guidance of the optimization direction, so as to obtain a distribution result. The distribution result is used for indicating the power borne by each energy supplier and the predicted power of the power utilization facility in each unit duration included in the target time period. By adopting the scheme, load distribution is performed according to the objective function under the constraint of various constraint conditions by creating the various constraint conditions and the objective function under the condition of multi-source cooperation, the flexibility is high, and the cost is low.

Description

Technical Field

[0001] This application relates to the field of energy system technology, and in particular to a load distribution method, device and readable storage medium. Background Technology

[0002] Currently, the power industry has a large number of power-consuming facilities, such as electric arc furnaces and data centers. Load forecasting and load allocation for these facilities are beneficial for optimizing power system scheduling and energy supply.

[0003] Load forecasting refers to predicting the power consumption of electrical facilities over a future period, such as predicting the power consumption of electrical facilities at each point in time over the next three days. Load allocation refers to the allocation of power from different energy suppliers to electrical facilities in a multi-source collaborative scenario. Traditional schemes allocate load according to fixed rules. For example, each energy supplier is assigned a fixed share, and each supplier provides power to energy-consuming parties such as electrical facilities according to its share. For instance, it might be stipulated that 80% of the power of electrical facilities is provided by the grid, and the remaining 20% ​​by renewable energy sources.

[0004] However, the aforementioned fixed load allocation method is inflexible and costly. Summary of the Invention

[0005] This application provides a load distribution method, device, and readable storage medium. By creating multiple constraints and objective functions, load distribution is performed according to the objective function under the constraints of multiple constraints, which is highly flexible and low-cost.

[0006] In a first aspect, this application provides a load sharing method, including: Create constraints that indicate the conditions that the power-consuming facilities and each energy supplier must meet within each unit of time in the target time period, provided that multiple energy suppliers are working together to provide energy to the power-consuming facilities within a target time period. The unit of time is obtained by dividing the target time period according to a preset time resolution. The multiple energy suppliers include at least a new energy supplier. Determine the objective function; Determine the optimization direction, which is the core guide for solving the objective function; Under the constraints of the stated conditions and the guidance of the optimization direction, the load of the power-consuming facilities is allocated according to the objective function to obtain the allocation result. The allocation result is used to indicate the power undertaken by each energy supplier and the predicted power of the power-consuming facilities within each unit of time in the target time period.

[0007] Secondly, this application provides a load distribution device, comprising: A creation module is used to create constraints, which indicate the conditions that the power-consuming facilities and each of the energy suppliers must meet within each unit of time in the target time period when multiple energy suppliers are cooperating to provide energy to the power-consuming facilities within the target time period. The unit of time is obtained by dividing the target time period according to a preset time resolution. The multiple energy suppliers include at least a new energy supplier. The first determining module is used to determine the objective function; The second determining module is used to determine the optimization direction, which is the core guide for solving the objective function. The processing module is configured to perform load allocation on the power-consuming facilities according to the objective function under the constraints of the constraints and the guidance of the optimization direction to obtain allocation results. The allocation results are used to indicate the power undertaken by each energy supplier and the predicted power of the power-consuming facilities within each unit of time in the target time period.

[0008] Thirdly, this application provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device implements the method described in the first aspect or various possible implementations of the first aspect.

[0009] Fourthly, this application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, are used to implement the method described in the first aspect or various possible implementations of the first aspect.

[0010] Fifthly, this application provides a computer program product containing a calculation program, which, when executed by a processor, implements the method described in the first aspect or various possible implementations of the first aspect. The load allocation method, device, and readable storage medium provided by this application allow the electronic device to create constraints, determine objective functions and optimization directions, and, under the constraints and guidance of the optimization directions, allocate loads to electrical facilities according to the objective function to obtain allocation results. The allocation results indicate the power borne by each energy supplier and the predicted power of the electrical facilities within each unit of time within the target time period. Using this approach, in the case of multi-source collaboration, by creating multiple constraints and objective functions, load allocation is performed according to the objective function under the constraints of multiple constraints, resulting in high flexibility and low cost. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of the energy supplier in the load allocation method provided in this application; Figure 2 This is a flowchart of the load sharing method provided in this application; Figure 3 A schematic diagram of the load distribution device provided in this application; Figure 4 A schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0013] Load allocation for electrical facilities refers to determining, at each point in time, how much power each energy supplier should provide to energy-consuming facilities, taking into account the capabilities of various energy suppliers.

[0014] Normally, electrical facilities are powered by the grid. During load distribution, a fixed amount of power is allocated to each facility based on its power capacity to ensure that the overall load does not exceed the grid's power supply limit. Compared to grid power, renewable energy is cleaner and cheaper. Therefore, utilizing renewable energy to power electrical facilities has become a new trend. Grid power is only considered when renewable energy cannot provide sufficient power. However, renewable energy is intermittent and fluctuates. If fixed rules are continued for load distribution, adaptability will be poor. For example, a sudden decrease in renewable energy supply can cause passive power outages for electrical facilities, leading to risks.

[0015] Therefore, in the case of multi-source coordination, how to allocate the load of power facilities has become an urgent problem to be solved.

[0016] Based on this, this application provides a load distribution method, device, and readable storage medium. In the case of multi-source collaboration, by creating a variety of constraints and objective functions, load distribution is performed according to the objective function under the constraints of the various constraints, which is highly flexible and low-cost.

[0017] The subject of this application is an electronic device, such as a server, desktop computer, or laptop. This electronic device locally stores supply capacity curves of various energy suppliers, various parameters of power-consuming facilities, and parameters of various energy suppliers. Energy suppliers include at least new energy sources.

[0018] In this application, the electrical facilities are high-energy-consuming equipment, including but not limited to data centers, submerged arc furnaces, refrigeration equipment, pumps, boilers, etc., and submerged arc furnaces include but are not limited to manganese silicon submerged arc furnaces and ferrosilicon submerged arc furnaces. Taking a submerged arc furnace as an example, the energy supplier may optionally include at least one of the following: grid power, energy storage cabinets, and exhaust gas tanks. For an example, please refer to [reference needed]. Figure 1 .

[0019] Figure 1 This is a schematic diagram of the energy supplier in the load sharing method provided in this application. Please refer to... Figure 1 The energy suppliers include at least new energy sources 11. Optionally, energy suppliers may also include grid power 12, energy storage cabinets 13, and exhaust gas tanks 14, etc., which work together to supply power to the electric arc furnace 15. New energy sources 11 may include, for example, wind farms, tidal power stations, and other energy systems.

[0020] When the power provided by the new energy source 11 is greater than the power required by the electric arc furnace 15, the excess power is stored in the energy storage cabinet 13; when the power provided by the new energy source 11, grid power 12, etc. is insufficient, the energy storage cabinet 13 supplies power to the electric arc furnace 15 to ensure the operation of the electric arc furnace 15.

[0021] The submerged arc furnace 15 may be, for example, a manganese silicon submerged arc furnace or a ferrosilicon submerged arc furnace; this application is not limited to these types. During the operation of the submerged arc furnace 15, when the power of the submerged arc furnace 15 reaches the lower limit for generating exhaust gas, the exhaust gas generated by the submerged arc furnace 15 can also be converted into electrical energy, which can be directly supplied to the submerged arc furnace 15; alternatively, the exhaust gas generated by the submerged arc furnace 15 can be stored in the exhaust gas tank 14. When various energy suppliers cooperate to supply power to the submerged arc furnace 15, the exhaust gas tank 14 can also act as an energy supplier, converting the stored exhaust gas into electrical energy and supplying it to the submerged arc furnace 15.

[0022] In this application, the electric arc furnace 15 is an electrical facility. The purpose of load allocation is to generate an allocation table in advance for a target time period. This allocation table indicates how much power each energy supplier provides to the electrical facility for each unit of time within the target time period. Based on this allocation table, the staff controls the power supplied to the electrical facility by each energy supplier, ensuring the stable operation of the electrical facility while using new energy sources as much as possible or ensuring production output.

[0023] In this application, "the energy supplier provides energy to the electrical facilities" means that the energy supplier supplies power to the electrical facilities; that is, providing energy can be referred to as supplying power, providing power, etc.

[0024] This approach utilizes multiple energy suppliers to collaboratively power electrical facilities, addressing the issues of high costs and carbon emissions associated with single-grid power supply. When renewable energy is abundant and surplus, it is stored in energy storage cabinets. When renewable energy is insufficient, the energy storage cabinets supply power to the electrical facilities, ensuring stable operation of the facilities and reducing their operating costs.

[0025] Figure 2 This is a flowchart of the load sharing method provided in this application. The execution subject of this embodiment is an electronic device, and this embodiment includes: 201. Create constraints, which indicate the conditions that the power-consuming facilities and the energy suppliers must meet within each unit of time in the target time period when multiple energy suppliers are cooperating to provide energy to the power-consuming facilities within the target time period. The unit of time is obtained by dividing the target time period according to a preset time resolution. The multiple energy suppliers include at least a new energy supplier.

[0026] In this application, the target time period is a future period of time during which load sharing is required. The duration can be 72 hours, a week, 10 days, a month, a quarter, half a year, a year, etc., and this application is not limited to this. For example, a target time period of March 1st to March 31st means load sharing for one month. Another example is a target time period of March 29th of this year to March 28th of next year, which means load sharing for one year. Yet another example is load sharing for the second half of the year.

[0027] In this application, the time resolution is, for example, 10 minutes, 15 minutes, 20 minutes, etc. The target time period is divided into multiple unit durations according to the target resolution, with each unit duration being 10 minutes, 15 minutes, etc. The purpose of load allocation is to predict the power provided by each energy supplier within each unit duration of the target time period, i.e., the actual power consumed by each energy supplier. For example, if the target time period is one day, and the unit duration is 10 minutes, then one day contains 144 unit durations. Through load allocation, electronic equipment determines the power provided by each energy supplier within each unit duration. Assuming the 144 unit durations are numbered sequentially from 1 to 144, taking unit duration number 50 as an example, the electronic equipment determines that within this unit duration, the renewable energy supplier provides 15,000 kilowatts of power at each time point within those 10 minutes, and the grid power provides 10,000 kilowatts of power at each time point within those 10 minutes. The 15,000 kilowatts and 10,000 kilowatts of power are also referred to as the power consumed.

[0028] It should be noted that this example uses a target time period of one day. In reality, the shortest start-up and shutdown cycle of electrical facilities is likely to be longer than one day. Taking an electric arc furnace as an example, the shortest start-up and shutdown cycle of an electric arc furnace is three days. That is to say, the minimum operating time that an electrical facility can maintain from startup to the permitted shutdown is three days. The target time period is at least the shortest start-up and shutdown cycle of the electrical facility.

[0029] In this application, "multiple energy suppliers collaboratively providing energy to electrical facilities" means that multiple energy suppliers simultaneously provide energy to electrical facilities within any unit of time during a target time period. For example, if renewable energy is sufficient within a unit of time, then only renewable energy will be used to provide energy to the electrical facilities. Conversely, if renewable energy is insufficient within a unit of time, then both renewable energy and energy storage units will simultaneously provide energy to the electrical facilities within that unit of time.

[0030] In this application, for any target time period, the electronic device predicts the power provided by each type of energy supply within each unit of time included in that target time period. For example, if a unit of time is 20 minutes, load allocation reveals that within those 20 minutes, the new energy source provides 20,000 kW of power, the energy storage unit provides 5,000 kW, and the exhaust gas tank provides 5,000 kW. That is, at each point in time within those 20 minutes, the new energy source continuously provides 20,000 kW of power, the energy storage unit provides 5,000 kW, and the exhaust gas tank provides 5,000 kW. In other words, within those 20 minutes, the power consumed by the new energy source, the energy storage unit, and the exhaust gas tank are 20,000 kW, 5,000 kW, and 5,000 kW, respectively.

[0031] In this application, there may be one or more constraints to regulate the conditions that electrical facilities and energy suppliers must meet within each unit of time. For example, the power consumption of renewable energy cannot exceed the predicted power consumption of renewable energy. The predicted power consumption of renewable energy refers to the predicted maximum power that renewable energy can provide; the power consumption of renewable energy refers to the actual power provided by the renewable energy supplier to electrical facilities such as blast furnaces.

[0032] 202. Determine the objective function.

[0033] In this application, the objective function is a function created for the purpose of load allocation, used to allocate the load of electrical facilities, that is, to determine the amount of power undertaken by each energy supplier within each unit of time. The constraints narrow the optimization range of the objective function from an infinite space to a feasible region; the objective function can only find its optimal solution within the feasible region. When the constraints are relaxed, the feasible region is larger; when the constraints are strict, the feasible region shrinks.

[0034] 203. Determine the optimization direction. The optimization direction is the core guide for solving the objective function.

[0035] In this application, the optimization direction is used to indicate the search direction of the objective function, serving as a final guide to clarify "what result to pursue," such as pursuing the highest production capacity of the ferroelectric furnace; or prioritizing the use of power provided by new energy suppliers.

[0036] 204. Under the constraints of the stated constraints and the guidance of the optimization direction, the load of the power-consuming facilities is allocated according to the objective function to obtain the allocation result. The allocation result is used to indicate the power undertaken by each energy supplier and the predicted power of the power-consuming facilities within each unit of time included in the target time period.

[0037] Electronic equipment solves an objective function based on constraints and the direction of optimization to obtain the load allocation result. This allocation result is, for example, an allocation table indicating the power consumed by each energy supplier and the predicted power of the electrical facilities within each unit of time. The predicted power indicates the power at which the electrical facilities operate within each unit of time.

[0038] The load allocation method provided in this application involves electronic devices creating constraints, determining objective functions and optimization directions, and then, guided by these constraints and the optimization direction, allocating loads to electrical facilities according to the objective function to obtain allocation results. These results indicate the power consumed by each energy supplier and the predicted power of the electrical facilities within each unit of time span included in the target time period. This approach, in multi-source collaborative scenarios, allows for high flexibility and low cost by creating multiple constraints and objective functions, and then allocating loads according to the objective function under these constraints.

[0039] In the above embodiments, within each unit of time, if the predicted power of the electrical facility meets the operating requirements, load allocation is required; if the predicted power of the electrical facility does not meet the operating requirements, load allocation is not required. The unit of time that meets the operating requirements forms the operating time period of the electrical facility, and the unit of time that does not meet the operating requirements forms the outage time period of the electrical facility. Therefore, when the electronic equipment allocates load to the electrical facility, it filters out some sliding windows, which are the operating time periods of the electrical facility. Then, the electronic equipment performs load allocation based on these sliding windows.

[0040] In one approach, electronic devices perform load modeling on electrical facilities. After obtaining the load model, load forecasting is performed based on it. This captures the patterns of load changes over time, environment, and user behavior, thus obtaining the predicted power of the facilities within each unit of time. Subsequently, the electronic devices further filter out sliding windows representing the operating time periods of the electrical facilities.

[0041] In another approach, during the process of the electronic device allocating load to the electrical facilities according to the objective function under the constraints of the stated constraints and the guidance of the optimization direction, the electronic device first slides a sliding window of a preset length, starting from the first unit of time in the target time period and following a preset step size. After each sliding window is slid, the electronic device, under the constraints of the stated constraints and the guidance of the optimization direction, allocates load to the electrical facilities according to the objective function to obtain the allocation result.

[0042] For example, a sliding window may contain multiple unit durations, such as 144 unit durations, each unit duration being 10 minutes long. The preset step size is not limited to, for example, 1 unit duration, 2 unit durations, etc. The length of the sliding window is the shortest start-stop cycle of the electrical facility; or, the length of the sliding window is a preset multiple of the shortest start-stop cycle.

[0043] The electronic device slides a window of preset length according to a preset step size. For example, if the target time period is one month, the unit duration is 20 minutes, and a month has 30 days containing 2160 units of duration, these units of duration are numbered sequentially as 1, 2, ... 2160. Taking a sliding window containing 72 units of duration as an example, the first sliding window contains units of duration 1 to 72, the second sliding window contains units of duration 2 to 73, the third sliding window contains units of duration 3 to 74, and so on.

[0044] In this application, the parameters to be solved in the objective function include the predicted power of the electrical facilities within each unit of time and the power undertaken by each energy supplier. After each sliding window slides, the electronic device can simultaneously determine the predicted power and the power undertaken by each energy supplier by solving the objective function.

[0045] This approach allows electronic devices to distribute load through a sliding window, which significantly reduces time complexity and computational costs, thereby improving the efficiency of load distribution.

[0046] Optionally, in the above embodiments, after each sliding window is slid, under the constraints of the constraints and the guidance of the optimization direction, the electronic device performs load allocation on the power-consuming facilities according to the objective function to obtain the allocation result. Then, it further determines whether the current sliding window meets preset conditions. If the current sliding window does not meet the preset conditions, the allocation result is saved. The preset conditions indicate that the number of target durations within multiple unit durations included in the sliding window is greater than a preset number, the predicted power of the power-consuming facilities within the target duration is less than the target power, and the allocation result also includes the predicted power corresponding to each unit duration within the current sliding window.

[0047] For each unit of time, if the predicted power is less than the target power, it indicates that the electrical facilities should ideally be out of service during that unit of time. Furthermore, the electrical facilities should not frequently switch between shutdown and startup. Therefore, the electronic equipment uses a sliding window as a unit to determine if there are a preset number of target periods within that window where the predicted power is less than the target power. If there are many units of time with lower predicted power within a sliding window, making it impossible to ensure the normal operation of the electrical facilities, the electronic equipment determines that the electrical facilities should be out of service for each unit of time included in that sliding window. It does not need to save the load allocation results for that sliding window; instead, it simply marks that sliding window as an outage period.

[0048] If the number of target durations within a sliding window where the predicted power is less than the target power is less than a preset number, or even if there are no target durations where the predicted power is less than the target power, it means that the predicted power corresponding to most unit durations within the sliding window meets the operational requirements of the power facilities, ensuring their normal operation. Therefore, the electronic device saves the load allocation results for this sliding window and marks this sliding window as an operating time period.

[0049] By adopting this scheme, the electronic device determines whether to save the load allocation result for the sliding window based on whether the number of target durations within the sliding window is less than a preset number, thereby improving the quality of load allocation.

[0050] The constraints and optimization directions in the above embodiments will be explained in detail below.

[0051] First, the constraints.

[0052] Optionally, the aforementioned constraints include power constraints for electrical facilities. During the process of creating constraints, the electronic device first obtains a power rise / fall rate table for the electrical facilities, which indicates the power variation pattern of the electrical facilities. Then, the electronic device determines the power constraints based on the power rise / fall rate table, which includes a first constraint, a second constraint, a third constraint, and a fourth constraint.

[0053] In this application, the power rise / fall rate table is a pre-set table that reflects the power change pattern of electrical facilities. The power rise / fall rate tables differ for different electrical facilities. Table 1 shows the power rise / fall rate table for manganese silicon submerged arc furnaces.

[0054]

[0055] Please refer to Table 1, where P1 < P2 < P3 < P4 < P5, a < b < c, a < d < b, upRamp represents the increase, and downRamp represents the decrease. A production cycle of a manganese silicon submerged arc furnace includes three stages: ramp-up, smelting, and tapping. When the predicted power of the manganese silicon submerged arc furnace in the previous unit time is greater than or equal to P1 kW but less than P2 kW, if the furnace is in the ramp-up stage, the predicted power in the current unit time will increase by a maximum of Δ4 kW and decrease by a maximum of Δ1 kW. If the furnace is in the smelting stage, the predicted power in the current unit time will increase by a maximum of Δ2 kW and decrease by a maximum of Δ3 kW.

[0056] After obtaining the power rise / fall rate table of the electric arc furnace, the electronic device determines a first constraint, a second constraint, a third constraint, and a fourth constraint. The first constraint indicates the interval constraint that the predicted power of the electrical facility must satisfy within the previous unit of time in the current unit of time; the second constraint indicates the interval constraint that the predicted power of the electrical facility must satisfy within the current unit of time; the third constraint indicates the constraint relationship between a first power consumption and a second power consumption, where the first power consumption is the cumulative power consumption of the electrical facility up to the current unit of time, and the second power consumption is the cumulative power consumption of the electrical facility up to the previous unit of time; the fourth constraint indicates the interval constraint that the first power consumption must satisfy. The first constraint is shown in formulas (1.1) and (1.2) below: Formula (1.1) Formula (1.2) in, This represents the predicted power consumption of electrical facilities within the previous unit of time. Indicates the power range. and This forms a power interval, such as power interval [P1, P2), power interval [P2, P3), etc., where N represents a total of 5 power intervals, and j represents the j-th power interval. This indicates the power range in which the electrical equipment is located. This indicates the power range of the electrical facilities in the previous unit of time. This indicates that the sum of the integer variables in each row and column is 1.

[0057] The second constraint is shown in the following formulas (1.3), (1.4), (1.5), and (1.2): Formula (1.3) Formula (1.4) Formula (1.5) Formula (1.2) in, This represents the predicted power consumption of electrical facilities within the previous unit of time. This represents the predicted power consumption of electrical facilities within the current unit of time. Indicates the power range of the electrical equipment. "Up to" indicates that the electrical facility was in the j-th power range during the previous unit of time, M represents the number of cumulative power consumption ranges, and i represents the i-th cumulative power consumption range. This represents the increase when the cumulative power consumption of the electrical facility up to the previous unit of time is in the i-th cumulative power consumption interval and the predicted power is in the j-th power interval. For example, if the cumulative power consumption is between 0 and a, meaning the electrical facility is in the ramp-up phase, and the predicted power of the electrical facility up to the previous unit of time is greater than P2, then... It is Δ2.

[0058] Similarly, This indicates the cumulative power consumption range of electrical facilities. This indicates that the cumulative power consumption of the electrical facilities up to the previous unit of time is in the i-th cumulative power consumption interval. This represents the decrease in power consumption when the cumulative power consumption of the electrical facility up to the previous unit of time is in the i-th cumulative power consumption interval and the predicted power is in the j-th power interval. For example, if the cumulative power consumption is between 0 and a, meaning the electrical facility is in the ramp-up phase, and the predicted power of the electrical facility up to the previous unit of time is greater than P2, then... It is Δ1.

[0059] The third constraint is shown in formula (1.6): Formula (1.6) in, This indicates the cumulative electricity consumption of the electrical facilities up to the previous unit of time. TR represents the cumulative power consumption of electrical facilities per unit time up to the present, where TR indicates the time resolution. This indicates the predicted power consumption of electrical facilities within the current unit of time.

[0060] The fourth constraint is shown in formulas (1.7) and (1.8) below: Formula (1.7) Formula (1.8) Formula (1.7) indicates that the cumulative power consumption of the electrical facility up to the current unit of time is within the (i-1)th cumulative power consumption interval and cannot exceed the ith cumulative power consumption interval. For example, the cumulative power consumption of the electrical facility in the current unit of time is between 0 and a; or the cumulative power consumption of the electrical facility in the current unit of time is between a and b, etc.

[0061] Using this approach, electronic devices create first, second, third, and fourth constraints through a power rise / fall rate table, achieving the goal of accurately creating power constraints while improving load distribution efficiency.

[0062] Optionally, when the power-consuming facility is a submerged arc furnace, in addition to power constraints, the constraints described in this application also include supply constraints and supply-demand balance constraints for each energy supplier. The supply-demand balance constraint indicates that the total power supplied by each energy supplier within the unit time period is equal to the total power demanded by each energy demander within the unit time period. The energy demanders include the power-consuming facility. Energy suppliers include, but are not limited to, new energy sources, grid power, energy storage cabinets, and exhaust gas tanks. The supply constraints of these energy suppliers are described below.

[0063] A. New energy constraints The supply constraint of new energy within the current unit time t is shown in the following formula (2): Formula (2) in, This represents the predicted power consumption of new energy sources within a given unit of time. This represents the predicted power output from renewable energy sources within the current unit of time. In other words, the power that renewable energy sources can provide within the current unit of time is... Of this power, the power supplied to energy demanders such as ferroelectric furnaces is... .

[0064] B. Grid power constraints The current power supply constraint within a unit time period t is shown in the following formula (3): Formula (3) in, This indicates the current power consumption of the grid per unit time. This represents the predicted power supplied by the grid within the current unit of time. In other words, the power that the grid can supply within the current unit of time is... Of this power, the power supplied to energy demanders such as ferroelectric furnaces is... .

[0065] C. Energy storage cabinet constraints The constraints of the energy storage cabinet include charging and discharging power constraints and charging and discharging saturation constraints. The charging and discharging power constraints are shown in formulas (4) and (5) below: Formula (4) Formula (5) in, This indicates the amount of charging done by the energy storage cabinet within the current unit of time. This indicates the discharge amount of the energy storage cabinet within the current unit of time. This indicates the power limit of a single charge and discharge cycle for the energy storage cabinet.

[0066] The saturation constraints for charging and discharging are shown in formulas (6) and (7) below: Formula (6) Formula (7) in, TR indicates the capacity of the energy storage unit within the previous unit of time, and TR indicates the time resolution. This indicates the lower limit of the energy storage cabinet capacity. This indicates the upper limit of the energy storage cabinet capacity.

[0067] C. Exhaust gas constraints Exhaust gas constraints include exhaust gas power constraints and charge / discharge saturation constraints. The exhaust gas electrical power constraints are shown in formulas (8.1) to (8.6): Formula (8.1) Formula (8.2) Formula (8.3) Formula (8.4) Formula (8.5) Formula (8.6) in, This indicates the lower limit of the power required for the electric arc furnace to produce exhaust gas. This indicates whether the electric arc furnace produces exhaust gas within the current unit of time. This represents the predicted power output of the electric arc furnace within the current unit of time. This represents the power output of the exhaust gas produced by the electric arc furnace within the current unit of time. This represents the proportion of the predicted power of the electric arc furnace converted into exhaust gas, where M represents a sufficiently large positive number in the Big M method, theoretically approaching infinity.

[0068] The saturation constraints for charging and discharging are shown in formulas (9) and (10) below: Formula (9) Formula (10) in, This indicates the capacity of the exhaust gas tank within the previous unit of time. This indicates the amount of charge in the exhaust gas tank within the current unit of time. TR represents the discharge amount of the exhaust gas tank within the current unit of time, and TR represents the time resolution. This indicates the upper limit of the exhaust gas tank capacity, and this indicates the lower limit of the exhaust gas tank capacity. .

[0069] In this application, supply and demand balance refers to: new energy power + grid power + energy storage discharge power + tail gas power + tail gas tank venting power = electric arc furnace power demand + auxiliary power consumption demand + energy storage charging power + tail gas tank filling power. The supply and demand balance constraint is shown in the following formula (11): Formula (11) Where K represents the number of electric arc furnaces. This represents the predicted power of the k-th submerged arc furnace. This represents the auxiliary power consumption of the k-th submerged arc furnace, which corresponds to the power consumption of the auxiliary equipment that must be turned on to operate the submerged arc furnace.

[0070] Alternatively, the objective function is shown in formula (12) below: Formula (12) In the above formula (12), It is the predicted power of the k-th submerged arc furnace within the current unit of time. , , , , All of these are quantities to be solved. This represents the revenue factor for the k-th submerged arc furnace. , , , , Let and represent the cost factors for new energy sources, grid power, energy storage cabinets, exhaust gas, and exhaust gas tanks, respectively. Under the constraints of the above-mentioned conditions and guided by the optimization direction, the electronic equipment solves the objective function to obtain... , , , Thus, the energy supply of each of the energy suppliers is obtained.

[0071] By adopting this approach, when electronic devices solve the objective function, they also consider the supply and demand balance constraints and the supply constraints of various energy suppliers. This makes the feasible region more accurate, reduces ineffective exploration, and makes the load allocation results more in line with actual needs. It avoids theoretically feasible but unrealistic allocation results, thereby improving the quality of load allocation.

[0072] Secondly, optimize the direction.

[0073] Optionally, in the process of determining the optimization direction of electronic equipment, when the benefit factor of the power consumption facility is much greater than the cost factor of each of the energy suppliers, the optimization direction is determined to be the highest capacity of the power consumption facility; when the cost factor of grid electricity among the multiple energy suppliers is much greater than the cost factor of other energy suppliers, the optimization direction is determined to be the largest proportion of green electricity.

[0074] In this application, different optimization directions result in different solutions. , , , Different. Users set the revenue factor of the power facilities and the cost factor of each energy supplier according to their needs. When the relationship between the revenue factor and each cost factor is as shown in the following formula (13), the electronic equipment determines the optimization direction to maximize the capacity of the power facilities.

[0075] Formula (13) Referring to formula (13), the benefit factor of the power supply facility is much greater than the cost factor of each energy supplier. Therefore, the optimization direction for electronic equipment is determined to be the highest capacity of the power supply facility.

[0076] When the cost factors of each energy supplier are related as shown in the following formula (14), the optimization direction of electronic equipment is determined to be the largest proportion of green electricity.

[0077] Formula (14) Please refer to formula (14). The cost factor of grid electricity is much greater than that of other energy suppliers. Therefore, the optimization direction for electronic equipment is to maximize the capacity of power-consuming facilities.

[0078] Using this approach, electronic devices can change the optimization direction of the objective function by adjusting the magnitude of the revenue factor and the cost factor of each energy supplier. This method is simple and highly accurate.

[0079] Optionally, in the above embodiments, during the process of solving the objective function, the electronic device further determines the power usage priority of each energy supplier, and / or, when the number of electrical facilities is at least two, determines the power allocation priority of each electrical facility. Then, guided by the constraints of the constraints, the optimization direction, the usage priority, and / or the allocation priority, the electronic device performs load allocation on the electrical facilities according to the objective function to obtain the allocation result.

[0080] For example, the priority is used as shown in the following formula (15): Formula (15) The allocation priority is shown in the following formula (16): Formula (16) In formulas (15) and (16), sort means sorting. Formula (15) means that the priority of using new energy, grid power, energy storage cabinet, exhaust gas, and exhaust gas tank decreases in that order, that is, the power provided by new energy is used first, and the power provided by grid power is used only when the power provided by new energy is insufficient, and so on; Formula (16) means that the allocation priority of the first power-consuming facility, the second power-consuming facility, the third power-consuming facility, etc. decreases in that, the first power-consuming facility is allocated power first, and the second power-consuming facility is allocated power only on the premise that the first power-consuming facility is operating stably, and so on.

[0081] This approach allows electronic devices to set the priority of energy suppliers and / or the allocation priority of electrical facilities based on cost factors, which is simple and highly flexible.

[0082] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0083] Figure 3 This is a schematic diagram of the load distribution device provided in this application. The load distribution device 300 includes: a creation module 31, a first determination module 32, a second determination module 33, and a processing module 34.

[0084] The creation module 31 is used to create constraints, which are used to indicate the conditions that the power-consuming facilities and each of the energy suppliers must meet within each unit of time in the target time period when multiple energy suppliers cooperate to provide energy to the power-consuming facilities within the target time period. The unit of time is obtained by dividing the target time period according to a preset time resolution. The multiple energy suppliers include at least new energy suppliers. The first determining module 32 is used to determine the objective function; The second determining module 33 is used to determine the optimization direction, which is the core guide for solving the objective function. Processing module 34 is used to perform load allocation on the power-consuming facilities according to the objective function under the constraints of the constraints and the guidance of the optimization direction to obtain allocation results. The allocation results are used to indicate the power undertaken by each energy supplier and the predicted power of the power-consuming facilities in each unit of time within the target time period.

[0085] In one feasible implementation, the constraints include power constraints of the electrical facility. The creation module 31 is used to obtain a power rise / fall rate table of the electrical facility, which indicates the power change pattern of the electrical facility. Based on the power rise / fall rate table, the power constraints are determined, including a first constraint, a second constraint, a third constraint, and a fourth constraint. The first constraint indicates the interval constraint that the predicted power of the electrical facility must satisfy within the previous unit of time in the current unit of time. The second constraint indicates the interval constraint that the predicted power of the electrical facility must satisfy within the current unit of time. The third constraint indicates the constraint relationship between a first power consumption and a second power consumption, where the first power consumption is the cumulative power consumption of the electrical facility up to the current unit of time, and the second power consumption is the cumulative power consumption of the electrical facility up to the previous unit of time. The fourth constraint indicates the interval constraint that the first power consumption must satisfy.

[0086] In one feasible implementation, the constraints further include supply and demand balance constraints and supply constraints of each of the energy suppliers. The supply and demand balance constraints are used to indicate that the total power provided by each of the energy suppliers within the unit time period is equal to the total power demanded by each of the energy demanders within the unit time period. The energy demanders include the electricity-consuming facilities.

[0087] In one feasible implementation, the second determining module 33 is used to determine the cost factor of each energy supplier and the revenue factor of the power consumption facility; when the revenue factor of the power consumption facility is much greater than the cost factor of each energy supplier, the optimization direction is determined to be the highest capacity of the power consumption facility; when the cost factor of grid power among the multiple energy suppliers is much greater than the cost factor of other energy suppliers, the optimization direction is determined to be the largest proportion of green electricity.

[0088] In one feasible implementation, the processing module 34 is used to determine the usage priority of the power provided by each of the energy suppliers, and / or, when the number of electrical facilities is at least two, determine the allocation priority of the power allocated to each of the electrical facilities; under the constraints of the constraints, the optimization direction, the usage priority and / or the allocation priority, the load is allocated to the electrical facilities according to the objective function to obtain the allocation result.

[0089] In one feasible implementation, the processing module 34 is used to slide a sliding window of a preset length with the first unit duration in the target time period as the starting point and according to a preset step size; after each sliding window is slid, under the constraints of the constraints and the guidance of the optimization direction, the power facilities are load-allocated according to the objective function to obtain the allocation result.

[0090] In one feasible implementation, after each sliding window is slid, the processing module 34, under the constraints of the constraints and the guidance of the optimization direction, performs load allocation on the power-consuming facilities according to the objective function to obtain the allocation result. Then, it further determines whether the current sliding window meets preset conditions. These preset conditions indicate that the number of target durations within multiple unit durations included in the sliding window is greater than a preset number, and the predicted power of the power-consuming facilities within the target duration is less than the target power. The allocation result also includes the predicted power corresponding to each unit duration within the current sliding window. When the current sliding window does not meet the preset conditions, the allocation result is saved.

[0091] In one feasible implementation, the plurality of energy suppliers also includes at least one of grid power, energy storage cabinets, and exhaust gas tanks.

[0092] The load distribution device provided in this application can perform the actions of the electronic devices in the above embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0093] Figure 4 A schematic diagram of the structure of the electronic device provided in this application. The electronic device 400 includes: Processor 41 and memory 42; The memory 42 stores computer instructions and test data; The processor 41 executes the computer instructions stored in the memory 42, causing the processor 41 to perform the load distribution method as described above.

[0094] The specific implementation process of processor 41 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0095] Optionally, the electronic device 400 also includes a communication component 43. The processor 41, memory 42, and communication component 43 can be connected via a bus 44.

[0096] This application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, are used to implement the load distribution method described above.

[0097] This application also provides a computer program product comprising a computer program that, when executed by a processor, implements the load distribution method described above.

[0098] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims. It should be understood that this application is not limited to the precise structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A load sharing method, characterized in that, Applied to electronic devices, the method includes: Create constraints that indicate the conditions that the power-consuming facilities and each energy supplier must meet within each unit of time in the target time period, provided that multiple energy suppliers are working together to provide energy to the power-consuming facilities within a target time period. The unit of time is obtained by dividing the target time period according to a preset time resolution. The multiple energy suppliers include at least a new energy supplier. Determine the objective function; Determine the optimization direction, which is the core guide for solving the objective function; Under the constraints of the stated conditions and the guidance of the optimization direction, the load of the power-consuming facilities is allocated according to the objective function to obtain the allocation result. The allocation result is used to indicate the power undertaken by each energy supplier and the predicted power of the power-consuming facilities within each unit of time in the target time period.

2. The method according to claim 1, characterized in that, The constraints include the power constraints of the electrical facilities, and the creation of constraints includes: Obtain a power increase / decrease rate table for the electrical equipment, the power increase / decrease rate table being used to indicate the power change pattern of the electrical equipment; Based on the power rise and fall rate table, the power constraints are determined, and the power constraints include a first constraint, a second constraint, a third constraint, and a fourth constraint. Wherein, the first constraint is used to indicate the interval constraint that the predicted power of the electrical facility must satisfy within the previous unit of time of the current unit of time; The second constraint is used to indicate the interval constraint that the predicted power of the electrical facility must satisfy within the current unit of time. The third constraint is used to indicate the constraint relationship between the first power consumption and the second power consumption, wherein the first power consumption is the cumulative power consumption of the power-consuming facility up to the current unit of time, and the second power consumption is the cumulative power consumption of the power-consuming facility up to the previous unit of time; The fourth constraint is used to indicate the interval constraint that the first power consumption needs to satisfy.

3. The method according to claim 1, characterized in that, The constraints also include supply and demand balance constraints and supply constraints of each of the energy suppliers. The supply and demand balance constraints are used to indicate that the total power provided by each of the energy suppliers within the unit time period is equal to the total power demanded by each of the energy demanders within the unit time period. The energy demanders include the electricity-consuming facilities.

4. The method according to any one of claims 1 to 3, characterized in that, The determination of the optimization direction includes: Determine the cost factor for each of the energy suppliers and the revenue factor for each power consumption facility; When the revenue factor of the power-consuming facility is much greater than the cost factor of each of the energy suppliers, the optimization direction is determined to be maximizing the capacity of the power-consuming facility. When the cost factor of grid electricity among the multiple energy suppliers is much greater than the cost factor of other energy suppliers, the optimization direction is determined to be maximizing the proportion of green electricity.

5. The method according to any one of claims 1 to 3, characterized in that, Under the constraints of the stated conditions and guided by the optimization direction, the load allocation of the electrical facilities according to the objective function to obtain the allocation result includes: Determine the priority of power usage provided by each of the energy suppliers, and / or, when the number of electrical facilities is at least two, determine the priority of power allocation for each of the electrical facilities; Guided by the constraints of the stated constraints, the optimization direction, the usage priority, and / or the allocation priority, the load of the electrical facilities is allocated according to the objective function to obtain the allocation result.

6. The method according to any one of claims 1 to 3, characterized in that, Under the constraints of the stated conditions and guided by the optimization direction, the load allocation of the electrical facilities according to the objective function to obtain the allocation result includes: Starting from the first unit of time in the target time period, slide a sliding window of a preset length according to a preset step size; After each sliding of the sliding window, under the constraints of the constraints and the guidance of the optimization direction, the load of the power facilities is allocated according to the objective function to obtain the allocation result.

7. The method according to claim 6, characterized in that, After each sliding of the sliding window, under the constraints of the conditions and the guidance of the optimization direction, and after load allocation of the power facilities according to the objective function to obtain the allocation result, the method further includes: Determine whether the current sliding window meets the preset conditions. The preset conditions are used to indicate that the number of target durations within the multiple unit durations contained in the sliding window is greater than the preset number, the predicted power of the power-consuming facility within the target duration is less than the target power, and the allocation result also includes the predicted power corresponding to each unit duration within the current sliding window. If the current sliding window does not meet the preset conditions, save the allocation result.

8. The method according to any one of claims 1 to 3, characterized in that, The multiple energy suppliers also include at least one of the following: grid power, energy storage cabinets, and exhaust gas tanks.

9. An electronic device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to implement the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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