Control methods and control devices, electronic equipment for new energy power systems

Abstract

This application proposes a control method, control device, electronic device, and non-transient computer-readable storage medium for a new energy power system. The control method includes acquiring planned data of the new energy power system within a predetermined future time period; determining a predetermined time rolling optimization model of the new energy power system based on the planned data; determining a multi-period rolling quadratic programming model corresponding to the predetermined time rolling optimization model; and determining control commands for the new energy power system based on the multi-period rolling quadratic programming model. According to an embodiment of this application, the predetermined time rolling optimization model of the new energy power system is determined based on the planned data, and the predetermined time rolling optimization model is transformed into a multi-period rolling quadratic programming model, thereby obtaining control commands for the power consumption of the new energy generator sets and electrolytic cells in the new energy power system. Under the premise of satisfying the power balance between power supply and load, the operating cost of the power system is minimized.

Description

Technical Field

[0001] This application relates to the field of power systems, and more specifically, to a control method and control device for a new energy power system, electronic equipment, and a non-transient computer-readable storage medium. Background Technology

[0002] With the increasing proportion of renewable energy generation, there is a growing need for power systems to possess strong flexible adjustment capabilities and highly intelligent operation and control capabilities. This includes aggregating and managing distributed generation resources with electricity consumption resources, utilizing internal resources to mitigate fluctuations in distributed energy and achieve greater market returns. Large-scale electrolytic hydrogen production from renewable energy sources can fully leverage renewable energy generation, enabling engineering demonstrations of integrated wind-solar-hydrogen-storage projects, and helping energy-intensive enterprises achieve low-carbon, green, and efficient collaborative development.

[0003] By integrating wind power, photovoltaic power, energy storage systems, and the external power grid, a multi-energy complementary system can be achieved on the power supply side. Through the enterprise's own AC or DC grid, a stable power load can be provided for downstream hydrogen electrolysis, forming a typical integrated source-grid-load-storage system. During a day's operation, this integrated system needs to fully utilize wind and solar power output for hydrogen production, reducing the need to purchase electricity from the grid to meet the demands of hydrogen electrolysis.

[0004] For example, when electricity prices are low or wind and solar power output is high, the electrolyzer can produce and store large quantities of hydrogen. When wind and solar power output is insufficient and electricity prices are high, the surplus capacity of the hydrogen storage tank can be used to supply hydrogen load, thereby reducing system operating costs. The usage principles and functions of energy storage systems are largely the same as those of hydrogen storage tanks. In particular, when wind and solar power output cannot maintain the electrolyzer's operation (hot standby, cold standby, and minimum power operation), power can be supplied through batteries, avoiding the need to purchase electricity and further increasing the economic efficiency of system operation. Summary of the Invention

[0005] This application aims to propose a control method and control device, electronic equipment, and non-transient computer-readable storage medium for a new energy power system, so as to minimize the operating cost of the integrated source-grid-load-storage system while meeting the hydrogen load demand.

[0006] According to one aspect of this application, a control method for a new energy power system is proposed, comprising: acquiring planned data of the new energy power system within a predetermined future time period; determining a predetermined time rolling optimization model of the new energy power system based on the planned data; determining a multi-period rolling quadratic programming model corresponding to the predetermined time rolling optimization model based on the predetermined time rolling optimization model; and determining control commands for the new energy power system based on the multi-period rolling quadratic programming model.

[0007] According to some embodiments, the planned data for the future predetermined time period includes the start-up plan, operation plan, shutdown plan, power generation plan, and / or power consumption plan for wind turbines, photovoltaic inverters, and electrolyzers in the new energy power system within the future predetermined time period.

[0008] According to some embodiments, the predetermined time rolling optimization model is shown in the following equation:

[0009]

[0010] Among them, f grid (P grid ,t)=S p C p P grid,t △t+S f C f P grid,t △t+S v C v P grid,t △t,

[0011] T is the total number of time periods within the predetermined time period.

[0012] N represents the total number of electrolytic cells in the new energy power system.

[0013] f ele,i (P ele,i,t Let P be the operating cost of the i-th electrolytic cell during time period t. ele,i,t Let be the power consumption of the i-th electrolytic cell during time period t.

[0014] u i,t u is the start / stop state variable of the electrolytic cell at the current moment. i,t-1 This is the start / stop state variable of the electrolytic cell at the previous moment, and its value is either 0 or 1.

[0015] C su,i For start-stop costs,

[0016] ρ w This is the penalty coefficient for wind power generation deviating from the predicted value.

[0017] This represents the predicted value for wind power generation during time period t.

[0018] P w,t This represents the actual value for wind power generation during time period t.

[0019] ρ p This is the penalty coefficient for photovoltaic power generation deviating from the predicted value.

[0020] This represents the predicted value for photovoltaic power generation during period t.

[0021] Ppv,t This represents the actual value of photovoltaic power generation during time period t.

[0022] c d,t This is the discharge weighting coefficient for energy storage batteries.

[0023] P es,d,t This refers to the discharge power of the energy storage battery.

[0024] c c,t The charging weighting factor for energy storage batteries.

[0025] P es,c,t The charging power of the energy storage battery,

[0026] f grid (P grid,t This represents the total cost of purchased electricity within the scheduled timeframe.

[0027] P grid,t The active power of the electrolytic cell during time period t.

[0028] △t is the time corresponding to each time period.

[0029] f grid (P grid,t This represents the total cost of purchased electricity over a predetermined period in the future.

[0030] S p S f S v They are 0-1 variables respectively.

[0031] C p C f C v These correspond to the peak electricity price, normal voltage, and valley voltage when purchasing electricity from the power grid, respectively.

[0032] According to some embodiments, the constraints of the predetermined time rolling optimization model include: real-time power balance constraints, power transmission constraints of power grid lines, upper and lower limits of electrolyzer power constraints, energy storage system constraints, and hydrogen storage tank constraints.

[0033] According to some embodiments, determining a multi-period rolling quadratic programming model corresponding to the predetermined time rolling optimization model includes: determining a state-space model corresponding to the predetermined time rolling optimization model; and establishing the multi-period rolling quadratic programming model using the state-space model.

[0034] According to some embodiments, the state-space model is shown in the following equation:

[0035]

[0036] Where x(t) is a state variable, including the energy storage SOC state, the energy storage tank capacity state, and the grid-purchased electricity power; x(t+1) is the state variable at the next moment; y(t) is an output variable, including the energy storage SOC state, the energy storage tank capacity state, and the grid-purchased electricity power; u(t) is a control variable, including the electrolyzer power consumption, the energy storage charging and discharging power, the wind power generation power, and the photovoltaic power generation power; r(t) is a disturbance variable, including the wind power generation fluctuation power, the photovoltaic power generation fluctuation power, and the electrolyzer power load fluctuation power; and A, B, C, and D are the state matrix coefficients of the state space model.

[0037] The multi-period rolling quadratic programming model is shown in the following equation:

[0038]

[0039] Where Y is the output of the prediction model, Y ref Δu represents the scheduling plan before the predetermined time, Δu represents the power regulation within the predetermined time, matrix G is the weight matrix of power system performance indicators for state variables, and matrix H is the weight matrix of power system performance indicators for control variables.

[0040] According to some embodiments, the control method further includes: calculating the deviation between the output variable and the real-time output data; and using the deviation to correct the state variable at the next moment.

[0041] According to some embodiments, the control commands include the power consumption of the electrolytic cell, the power of energy storage charging and discharging, the power of wind power generation, and the power of photovoltaic power generation.

[0042] According to one aspect of this application, a control device for a new energy power system is proposed, comprising: a planned data acquisition unit for acquiring planned data of the new energy power system within a predetermined future time period; a rolling optimization model generation unit for determining a predetermined time rolling optimization model of the new energy power system based on the planned data; a multi-period rolling quadratic programming model generation unit for determining a multi-period rolling quadratic programming model corresponding to the predetermined time rolling optimization model based on the predetermined time rolling optimization model; and a control command generation unit for determining control commands of the new energy power system based on the multi-period rolling quadratic programming model.

[0043] According to one aspect of this application, an electronic device is provided, comprising: a processor; and a memory storing a computer program that, when executed by the processor, causes the processor to perform the control method as described in any of the preceding embodiments.

[0044] According to one aspect of this application, a non-transitory computer-readable storage medium is provided, on which computer-readable instructions are stored, which, when executed by a processor, cause the processor to perform the control method as described in any of the preceding embodiments.

[0045] According to the embodiments of this application, a predetermined time rolling optimization model of the new energy power system is determined based on the planned data, and the predetermined time rolling optimization model is transformed into a multi-period rolling quadratic programming model. This yields control commands for the power consumption of the new energy generator sets and electrolytic cells in the new energy power system, thereby minimizing the operating cost of the power system while ensuring the balance between power supply and load.

[0046] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The above and other objectives, features, and advantages of this application will become more apparent by referring to the accompanying drawings and describing exemplary embodiments in detail.

[0048] Figure 1 A flowchart of a control method for a new energy power system according to an example embodiment of this application is shown.

[0049] Figure 2 A flowchart illustrating the solution process of a state-space model according to an example embodiment of this application is shown.

[0050] Figure 3 A block diagram of a control device for a new energy power system according to an example embodiment of this application is shown.

[0051] Figure 4 An electronic device according to an exemplary embodiment of this application is shown. Detailed Implementation

[0052] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same drawings in the figures show the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0053] The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of these specific details, or other methods, components, materials, apparatus, or operations may be employed. In these cases, well-known structures, methods, apparatuses, implementations, materials, or operations will not be shown or described in detail.

[0054] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0055] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0056] As mentioned earlier, when electricity prices are low or wind and solar power output is high, hydrogen storage or energy storage systems can be used to produce and store large quantities of hydrogen in electrolyzers. When wind and solar power output is insufficient and electricity prices are high, the surplus capacity of hydrogen storage tanks or batteries can be used to supply power, thereby avoiding the need to purchase electricity, reducing system operating costs and further increasing the economic efficiency of system operation.

[0057] This application finds that, in practice, it is necessary to formulate wind power output plans, photovoltaic power output protection plans, energy storage system charge and discharge plans, and electrolysis hydrogen production power consumption plans for the future within a preset time frame, based on the start-up and shutdown plans, power generation plans, and power consumption plans of various source and load equipment, combined with ultra-short-term power forecast data of new energy sources; and to optimize the power setpoints of the electrolysis hydrogen production operating load conditions according to economic considerations, so as to minimize the system's operating cost while meeting hydrogen load demand.

[0058] In practice, intraday rolling optimization scheduling refers to formulating wind power output plans, photovoltaic power output protection plans, energy storage system charge and discharge plans, and electrolysis hydrogen production power consumption plans for 16 time periods (15 minutes per point) within the next 4 hours, based on the start-up and shutdown plans, power generation plans, and power consumption plans of various source and load equipment, combined with ultra-short-term power forecast data of new energy sources. It can also optimize the power setpoints of the electrolysis hydrogen production operating load conditions according to economic considerations, so as to minimize the system's operating cost while meeting hydrogen load demand.

[0059] According to an embodiment of this application, a predetermined time rolling optimization model for the new energy power system is determined based on the planned data, and the predetermined time rolling optimization model is transformed into a multi-period rolling quadratic programming model to obtain the control instructions for the new energy power system. Under the premise of meeting the power system demand, the operating cost of the power system is minimized.

[0060] The specific embodiments according to this application will now be described in detail with reference to the accompanying drawings.

[0061] Figure 1 A flowchart illustrating a control method for a new energy power system according to an example embodiment of this application is shown, such as... Figure 1 The control method shown includes steps S101, S103, S105, and S107. The following will use... Figure 1 Taking an example, a control method for a new energy power system according to an example embodiment of this application will be described in detail.

[0062] like Figure 1 As shown, in step S101, the planned data of the new energy power system within a predetermined time period is obtained.

[0063] By acquiring planned data from the new energy power system for a predetermined period in the future, plans for wind power output, photovoltaic power output, energy storage system charge / discharge curves, and hydrogen electrolysis power consumption are formulated for the predetermined period. In some embodiments, the planned data for the predetermined period in the future includes the start-up plans, operation plans, shutdown plans, power generation plans, and / or power consumption plans for wind turbines, photovoltaic inverters, and electrolyzers in the new energy power system for the predetermined period in the future.

[0064] According to embodiments of this application, based on the current start-up and shutdown plans (including start-up and shutdown plans), power generation plans, and power consumption plans of various source and load devices, combined with ultra-short-term power forecast data of new energy sources, a wind power output plan, photovoltaic power output enhancement plan, energy storage system charge-discharge plan curve, and electrolysis hydrogen production power consumption plan are formulated for a predetermined period in the future (e.g., 16 time periods in 15-minute intervals within the next 4 hours) for intraday rolling optimization scheduling. The source and load devices include, but are not limited to, wind turbines, photovoltaic inverters, energy storage batteries, and electrolyzers.

[0065] In a specific embodiment, the current-day start-up and shutdown plan for new energy generator sets and electrolyzers is a plan for the start-up, operation, or shutdown status of various types of equipment, including wind turbines, photovoltaic inverters, and electrolyzers, at 15-minute intervals for a total of 96 time intervals, as well as power generation and power consumption plan data.

[0066] In some embodiments, the current-day start-up and shutdown plan for new energy generator sets and electrolyzers includes the status of wind turbines, the status of photovoltaic inverters, the status of electrolyzers, the power generation plan of wind turbines, the power generation plan of photovoltaic inverters, and the power consumption plan of electrolyzers. Specifically, the status of wind turbines includes startup, power generation, maintenance, and shutdown; the status of photovoltaic inverters includes startup, regulation, fault, and shutdown; the status of electrolyzers includes cold standby, hot standby, operation, and idle; the power generation plan of wind turbines refers to the active power value of units in power generation status at various time periods, with a data point every 15 minutes for the next day, for a total of 96 data points; the power generation plan of photovoltaic inverters refers to the active power value of photovoltaic units in regulation status at various time periods, with a data point every 15 minutes for the next day, for a total of 96 data points; the power consumption plan data of electrolyzers refers to the total power consumption of all electrolyzers in operation, hot standby, and idle statuses, with a data point every 15 minutes for the next day, for a total of 96 data points.

[0067] In a specific embodiment, the ultra-short-term power forecast data for photovoltaic and wind power generation, and the ultra-short-term hydrogen production load forecast data are obtained from the power forecasting systems of the photovoltaic and wind power generation stations, while the hydrogen production load forecast data for the electrolyzers are obtained from the load forecasting system of the energy management system. In practice, the ultra-short-term forecast data is selected as 16 points of ultra-short-term forecast data, with one point every 15 minutes for the next 4 hours.

[0068] In step S103, the predetermined time rolling optimization model of the new energy power system is determined based on the planned data.

[0069] When determining the rolling optimization model of the new energy power system based on planned data, it is necessary to satisfy the coupling and coordinated interaction relationships of the new energy hydrogen production process. In some embodiments, the coupling and coordinated interaction relationships of the new energy hydrogen production process include electrolyzer production constraints, electrolyzer state transition constraints, and / or the conversion relationship between hydrogen consumption and electrolyzer power consumption.

[0070] For example, the production constraints of the electrolytic cell as shown in formula (1).

[0071]

[0072] In the formula: A1 represents the sum of hydrogen rates during time interval t, in mol / h; A2 and A3 are characteristic parameters of power and gas production rate; A4 is the penalty coefficient for gas production rate. These represent the standby and operating states of the alkaline electrolytic cell n during time period t, respectively. For binary variables, from Switch to The value is 1 for one time period and 0 for other time periods. The active power of electrolytic cell n in time period t; This represents the standby power of the alkaline electrolyzer n during time period t.

[0073] For example, the state transition constraint of the electrolytic cell as shown in formula (2).

[0074]

[0075] in, and The binary variables represent the production state, standby state, and idle state respectively. Formula (2) can ensure that the two states cannot occur at the same time.

[0076] In practice, based on the characteristic curve between the power consumption and gas production of the electrolyzer, and under the constraints shown in formulas (1) and (2), the conversion between hydrogen consumption and electrolyzer power consumption is performed, i.e., the hydrogen production load of each electrolyzer unit is shown in formula (3). The characteristic curve between the power consumption and gas production of the electrolyzer is obtained by fitting historical operating data.

[0077]

[0078] in, This represents the gas production of the nth electrolyzer during time period t. This represents the power consumption of the nth electrolytic cell during time period t.

[0079] Under the conditions of satisfying the above constraints, the predetermined time rolling optimization model is obtained as shown in formula (4).

[0080]

[0081] Among them, f grid (P grid ,t)=S p C p P grid,t △t+S f C f P grid,t △t+S v C v P grid,t△t, T is the total number of time periods within the predetermined time, N is the total number of electrolytic cells in the new energy power system, f ele,i (P ele,i,t Let P be the operating cost of the i-th electrolytic cell during time period t. ele,i,t Let u be the power consumption of the i-th electrolytic cell during time period t. i,t u is the start / stop state variable of the electrolytic cell at the current moment. i,t-1 This is the start / stop state variable of the electrolytic cell at the previous moment, with a value of 0 or 1, C. su,i For start-up and shutdown costs, ρ w This is the penalty coefficient for wind power generation deviating from the predicted value. P is the predicted value for wind power generation during time period t. w,t ρ is the actual value of wind power generation during time period t. p This is the penalty coefficient for photovoltaic power generation deviating from the predicted value. P is the predicted value of photovoltaic power generation during period t. pv,t c is the actual value of photovoltaic power generation during time period t. d,t P is the discharge weighting coefficient for energy storage batteries. es,d,t c represents the discharge power of the energy storage battery. c,t P is the charging weighting factor for energy storage batteries. es,c,t The charging power of the energy storage battery, f grid (P grid,t P represents the total cost of purchased electricity within the scheduled timeframe. grid,t The active power of the electrolyzer during time period t, where Δt is the time corresponding to each time period, f grid (P grid,t This represents the total cost of purchased electricity within a predetermined future timeframe.

[0082] In a specific implementation, T represents the total number of time periods within a day, with each time period lasting 15 minutes, and T is 16. Δt represents 15 minutes, and f... grid (P grid,t () represents the total day-ahead cost of purchased electricity.

[0083] In some embodiments, f grid (P grid,t ) represents the total daily cost of purchased electricity, as shown in formula (5).

[0084] f grid (P grid ,t)=S p C p P grid,t △t+S f C f P grid,t △t+S v C v P grid,t △t (5)

[0085] In other embodiments, when formula (4) takes into account peak-valley flat electricity pricing, S p S f S v These are variables ranging from 0 to 1, corresponding to peak electricity price periods, flat electricity price periods, and off-peak electricity price periods, respectively. When operating under peak electricity price conditions: S p Equal to 1, S f S v All are zero; when operating at normal electricity prices: S f Equal to 1, S p S v All are zero; when running during off-peak hours: S v Equal to 1, S f S p All are zero; C p C f C v These correspond to the peak electricity price, normal voltage, and valley voltage of the power grid, respectively, with the unit being yuan / (kw.h).

[0086] In other embodiments, Equation (4) also needs to satisfy constraints including real-time power balance constraints, power transmission constraints of power grid lines, upper and lower limits of electrolyzer power constraints, energy storage system constraints, and hydrogen storage tank constraints.

[0087] In some embodiments, the real-time power balance constraint is as shown in equation (6).

[0088] P ele,i,t =P w,t +P s,t +P es,d,t +P grid,t -P es,c,t (6)

[0089] Among them, P ele,i,t P w,t P s,t P es,d,t P grid,t P es,c,t These represent the power consumption of the electrolytic cell, the power generation of wind power, the power generation of photovoltaic power, the discharge power of the energy storage battery, the power output to the grid, and the charging power of the energy storage battery at time t, respectively.

[0090] In other embodiments, the power transmission constraints of the power grid lines are as shown in Equation (7).

[0091]

[0092] in, These represent the minimum and maximum electricity purchases from the power grid during time period t, respectively, in kW.h.

[0093] In other embodiments, the upper and lower limits of the electrolytic cell power are constrained as shown in formula (8).

[0094]

[0095] in, S represents the standby power of alkaline electrolyzer i during time period t, in kW. i,t L i,t These represent the standby and operating states of alkaline electrolytic cell i during time period t, respectively. These represent the minimum and maximum operating power of the alkaline electrolytic cell n, in kW.

[0096] In other embodiments, the energy storage system constraints include upper and lower limits for the energy storage system's charge and discharge power and upper and lower limits for the energy storage system's battery capacity. The upper and lower limits for the energy storage system's charge and discharge power are shown in Equation (9), and the upper and lower limits for the energy storage system's battery capacity are shown in Equation (10).

[0097]

[0098] in, and These are the maximum limits for charging and discharging power of the energy storage battery, respectively; P es,d,t and P es,c,t The discharge power and charging power corresponding to time period t.

[0099] The upper and lower limits of the battery capacity of the energy storage system are constrained as shown in formula (10).

[0100]

[0101] Among them, S bat,t This refers to the state of charge of the energy storage battery during time period t. and These are the minimum and maximum state of charge values ​​for the energy storage battery;

[0102] In other embodiments, the hydrogen storage tank is constrained as shown in formula (11).

[0103]

[0104] in, The pressure of the hydrogen storage unit at the beginning of time period t is expressed in MPa. and These are the minimum and maximum pressure constraints for the hydrogen storage unit, respectively, in MPa.

[0105] In step S105, a multi-period rolling quadratic programming model corresponding to the predetermined time rolling optimization model is determined based on the predetermined time rolling optimization model.

[0106] According to an embodiment of this application, in step S105, firstly, a state-space model corresponding to the predetermined time rolling optimization model is determined based on the predetermined time rolling optimization model. Then, a multi-period rolling quadratic programming model is established using the state-space model.

[0107] In a specific embodiment, using ultra-short-term power prediction and ultra-short-term load prediction data within a preset time period, based on a finite time domain and using historical data and measured data, a state-space model corresponding to the predetermined time rolling optimization model shown in formula (4) is obtained, as shown in formula (12).

[0108]

[0109] Where x(t) are state variables, including the energy storage SOC state, the energy storage tank capacity state, and the grid-purchased electricity power; y(t) are output variables, including the energy storage SOC state, the energy storage tank capacity state, and the grid-purchased electricity power; u(t) are control variables, including the electrolyzer power consumption, the energy storage charging and discharging power, the wind power generation power, and the photovoltaic power generation power; r(t) are disturbance variables, including the wind power generation fluctuation power, the photovoltaic power generation fluctuation power, and the electrolyzer power load fluctuation power; and A, B, C, and D are the state matrix coefficients of the state space model.

[0110] In some embodiments, the state variable includes the energy storage state of charge (SOC). t Energy storage tank capacity status SOHC t Online purchase of electricity P grid，t As shown in formula (13)

[0111] x(t)=[SOC t SOHC t P grid,t (13)

[0112] In other embodiments, the output variable reference value includes the energy storage SOC (State of Charge) state. p_t Energy storage tank capacity status SOHC p_t And online shopping power P P_grid，t As shown in formula (14).

[0113] y(t)=[SOC p_t SOHC p_t P P_grid,t (14)

[0114] In other embodiments, the control variable includes the electrical power P used in the electrolyzer. ele，t Energy storage charging and discharging power P es,t Wind power generation capacity P w，t and photovoltaic power generation P pv，tAs shown in formula (15).

[0115] u(t)=[P ele,t P es,t P w,t P pv,t (15)

[0116] In other embodiments, the disturbance variable includes the wind power fluctuation ΔP. w，t Photovoltaic power generation fluctuation ΔP pv，t and the power fluctuation ΔP of the electrolytic cell's electrical load ele，t As shown in formula (16).

[0117] r(t) = [△P w,t △P pv,t △P ele,t (16)

[0118] According to an embodiment of this application, the objective function of formula (12) is a multi-period rolling quadratic programming model as shown in formula (17).

[0119]

[0120] Where Y is the output of the prediction model, Y ref The scheduling plan is set before the predetermined time, and Δu is the power adjustment within the predetermined time. The change of the weight factor in the weight matrix G affects the output of the prediction model shown in formula (17). In practice, the average error between the output of the prediction model shown in formula (17) and the power consumption plan curve can be adjusted by changing the weight factor in the weight matrix G until the preset accuracy requirement is met. In some other embodiments, the weight factor in the weight matrix H is related to the power consumption of the electrolytic cell. The larger the weighting factor in the weight matrix H, the stronger the control of the corresponding electrolytic cell and the smaller the change in the power consumption of the electrolytic cell. In some embodiments, the constraints of formula (17) are shown in formulas (18) to (20).

[0121] y min ≤y(t)≤y max (18)

[0122] u min ≤u(t)≤u max (19)

[0123] △u min ≤△u(t)≤△u max (20)

[0124] Among them, y min y max These are the minimum and maximum values ​​of the output variable, respectively; u min umax These are the minimum and maximum values ​​of the control variable, respectively; △u min , △u max These represent the minimum and maximum values ​​of the control variable increment, respectively. In practice, the output variable, control variable, and the maximum and minimum values ​​of the control variable increment can be preset as needed.

[0125] In step S107, the control commands for the new energy power system are determined based on the multi-period rolling quadratic programming model.

[0126] According to an embodiment of this application, in step S107, formula (17) is solved to obtain a multi-period rolling quadratic programming model. In practice, the control commands include the power consumption of the electrolytic cell, the charging and discharging power of the energy storage, the power generation of wind power, and the power generation of photovoltaic power.

[0127] according to Figure 1 The embodiments shown herein, according to the embodiments of this application, determine the predetermined time rolling optimization model of the new energy power system based on the planned data, and transform the predetermined time rolling optimization model into a multi-period rolling quadratic programming model, thereby obtaining the control commands for the power consumption of the new energy power system's new energy generator sets and electrolytic cells, so as to minimize the operating cost of the power system under the premise of satisfying the power balance between the power supply and the load.

[0128] Figure 2 A flowchart illustrating the solution process of a state-space model according to an example embodiment of this application is shown, as follows: Figure 2 As shown, in steps S201 to S203, the reference value of the output variable y(t), the state variable x(t) and the disturbance variable r(t) are sequentially input into the state space model determined by formula (12) to obtain the QP standard form mentioned in step S204, which is the multi-period rolling quadratic programming model shown by formula (17).

[0129] In step S205, it is determined whether formula (17) has a solution.

[0130] For example, the branch and bound method is used to solve formula (17), and the cutting plane is used to tighten the linear programming relaxation. Step S207 is executed to obtain the feasible solution of the current control quantity u(t), as shown in formula (21).

[0131] In step S207, the current control quantity is calculated.

[0132] U(k)=U(k-1)+δ(k) (21)

[0133] Where δ(k) is the power adjustment increment required for a predetermined time interval.

[0134] According to other embodiments, if formula (17) has no solution in S205, then step S209 is executed to modify the perturbation variable. In step S211, the first normally operating electrolytic cell is first switched from the working state (w=1) to the hot standby state (l=0), and steps S204 and S205 are executed to determine whether formula (17) has a solution; if there is no solution, the second electrolytic cell is switched from the working state to the hot standby state. This process continues until the QP problem has a solution; if all electrolytic cells have been switched to the hot standby state, it means that formula (17) has no feasible solution.

[0135] According to an embodiment of this application, after step S207, step S213 is executed to determine whether the power system needs further optimization, and then feedback correction is performed on the power system.

[0136] For example, by using the deviation between the model output and the real-time output, the deviation is iterated over until the deviation meets the predetermined requirements. The correction function is defined as shown in formula (22).

[0137]

[0138] Where e(t) is the deviation between the model output and the real-time output; x(t+1) is the state variable at time t+1, and x(t+1|t) is the expected value of the future state at time t; λ t The weighting coefficient for the error can be preset.

[0139] According to the embodiments of this application, the set values ​​of the control variables at time t+1, namely the power consumption of the electrolytic cell, the charging and discharging power of the energy storage, the power of the wind power generation, and the power of the photovoltaic power generation, are used as the control commands for real-time control in the next control cycle. The actual output of the unit at time t+1 is used as the initial value of the intraday optimization model, and a new model is generated iteratively to perform a new round of optimization scheduling calculations.

[0140] The above description primarily focuses on the methodological aspects of the embodiments of this application. Those skilled in the art should readily recognize that, based on the operations or steps described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Those skilled in the art can implement the described functionality in different ways for each specific operation or method, and such implementations should not be considered beyond the scope of this application.

[0141] The apparatus embodiments of this application are described below. For details not described in the apparatus embodiments of this application, please refer to the method embodiments of this application.

[0142] Figure 3 A block diagram of a control device for a new energy power system according to an example embodiment of this application is shown, such as... Figure 3The control device shown includes a planned data acquisition unit 301, a rolling optimization model generation unit 303, a multi-period rolling quadratic programming model generation unit 305, and a control command generation unit 307. Specifically, the planned data acquisition unit 301 acquires planned data for the new energy power system within a predetermined future timeframe; the rolling optimization model generation unit 303 determines a predetermined-time rolling optimization model for the new energy power system based on the planned data; the multi-period rolling quadratic programming model generation unit 305 determines a multi-period rolling quadratic programming model corresponding to the predetermined-time rolling optimization model; and the control command generation unit 307 determines control commands for the new energy power system based on the multi-period rolling quadratic programming model.

[0143] Figure 4 An electronic device according to an exemplary embodiment of this application is shown. Reference is made below. Figure 4 To describe an electronic device 200 according to this embodiment of the present application. Figure 4 The electronic device 200 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0144] like Figure 4 As shown, the electronic device 200 is presented in the form of a general-purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one storage unit 220, a bus 230 connecting different system components (including storage unit 220 and processing unit 210), a display unit 240, etc.

[0145] The storage unit stores program code, which can be executed by the processing unit 210 to perform the methods described in this specification according to various exemplary embodiments of this application. For example, the processing unit 210 can perform, for example... Figure 1 The method shown.

[0146] Storage unit 220 may include readable media in the form of volatile storage units, such as random access memory (RAM) 2201 and / or cache memory 2202, and may further include read-only memory (ROM) 2203.

[0147] Storage unit 220 may also include a program / utility 2204 having a set (at least one) program module 2205, such program module 2205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0148] Bus 230 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0149] Electronic device 200 can also communicate with one or more external devices 300 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 200, and / or with any device that enables electronic device 200 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 250. Furthermore, electronic device 200 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of electronic device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0150] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. The technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this application.

[0151] Software products may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0152] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0153] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0154] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the aforementioned functions.

[0155] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0156] According to an embodiment of this application, a computer program is proposed, including a computer program or instructions, which, when executed by a processor, can perform the methods described above.

[0157] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. Furthermore, any changes or modifications made by those skilled in the art based on the ideas of this application, and on the specific implementation methods and application scope of this application, are all within the scope of protection of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A control method for a new energy power system, characterized in that, include: Obtain the planned data of the new energy power system within a predetermined future time period; Based on the planned data, determine the predetermined time rolling optimization model for the new energy power system; Based on the predetermined time rolling optimization model, determine the multi-period rolling quadratic programming model corresponding to the predetermined time rolling optimization model; The control commands for the new energy power system are determined based on the multi-period rolling quadratic programming model. The predetermined time rolling optimization model is shown in the following equation: ， Among them, f grid (P grid ,t)=S p C p P grid,t △t+S f C f P grid,t △t+S v C v P grid,t △t, T is the total number of time periods within the predetermined time period. N represents the total number of electrolytic cells in the new energy power system. f ele,i (P ele,i,t Let P be the operating cost of the i-th electrolytic cell during time period t. ele,i,t Let be the power consumption of the i-th electrolytic cell during time period t. u i,t u is the start / stop state variable of the electrolytic cell at the current moment. i,t-1 This is the start / stop state variable of the electrolytic cell at the previous moment, and its value is either 0 or 1. C su,i For start-stop costs, ρ w This is the penalty coefficient for wind power generation deviating from the predicted value. This represents the predicted value for wind power generation during time period t. P w,t This represents the actual value for wind power generation during time period t. ρ p This is the penalty coefficient for photovoltaic power generation deviating from the predicted value. This represents the predicted value for photovoltaic power generation during period t. P pv,t This represents the actual value of photovoltaic power generation during time period t. c d,t This is the discharge weighting coefficient for energy storage batteries. P es,d,t This refers to the discharge power of the energy storage battery. c c,t The charging weighting factor for energy storage batteries. P es,c,t The charging power of the energy storage battery, f grid (P grid,t This represents the total cost of purchased electricity within the scheduled timeframe. P grid,t The active power of the electrolytic cell during time period t. △t is the time corresponding to each time period. f grid (P grid,t This represents the total cost of purchased electricity over a predetermined period in the future. S p S f S v They are 0-1 variables respectively. C p C f C v These correspond to the peak-hour electricity price, normal voltage, and valley-hour voltage when purchasing electricity from the power grid, respectively. Determine the multi-period rolling quadratic programming model corresponding to the predetermined time rolling optimization model based on the predetermined time rolling optimization model, including: Determine the state space model corresponding to the predetermined time rolling optimization model based on the predetermined time rolling optimization model; The multi-period rolling quadratic programming model is established using the state-space model. The multi-period rolling quadratic programming model is shown in the following equation: Where Y is the output of the prediction model, Y ref Δu represents the scheduling plan before the predetermined time, Δu represents the power regulation within the predetermined time, matrix G is the weight matrix of power system performance indicators for state variables, and matrix H is the weight matrix of power system performance indicators for control variables.

2. The control method according to claim 1, characterized in that, The planned data for the future predetermined time period includes the start-up plan, operation plan, shutdown plan, power generation plan, and / or power consumption plan for wind turbines, photovoltaic inverters, and electrolyzers in the new energy power system within the future predetermined time period.

3. The control method according to claim 2, characterized in that, The constraints of the predetermined time rolling optimization model include: real-time power balance constraints, power transmission constraints of power grid lines, upper and lower limits of electrolyzer power constraints, energy storage system constraints, and hydrogen storage tank constraints.

4. The control method according to claim 3, characterized in that, The state-space model is shown in the following equation: Where x(t) is a state variable, including the energy storage SOC state, the energy storage tank capacity state, and the grid-purchased electricity power; x(t+1) is the state variable at the next time step; y(t) is an output variable, including the energy storage SOC state, the energy storage tank capacity state, and the grid-purchased electricity power; u(t) is a control variable, including the electrolyzer power consumption, the energy storage charging and discharging power, the wind power generation power, and the photovoltaic power generation power; r(t) is a disturbance variable, including the wind power generation fluctuation power, the photovoltaic power generation fluctuation power, and the electrolyzer power load fluctuation power; and A, B, C, and D are the state matrix coefficients of the state space model.

5. The control method according to claim 4, characterized in that, Also includes: Calculate the deviation between the output variable and the real-time output data; The deviation is used to correct the state variables at the next time step.

6. The control method according to claim 1, characterized in that, The control commands include the power consumption of the electrolytic cell, the charging and discharging power of the energy storage, the power generation of wind power, and the power generation of photovoltaic power.

7. A control device for a new energy power system, characterized in that, The apparatus is used to implement the method as described in any one of claims 1-6, the apparatus comprising: The planned data acquisition unit is used to acquire planned data of the new energy power system within a predetermined future time period. A rolling optimization model generation unit is used to determine a predetermined time rolling optimization model for the new energy power system based on the planning data. A multi-period rolling quadratic programming model generation unit is used to determine a multi-period rolling quadratic programming model corresponding to the predetermined time rolling optimization model based on the predetermined time rolling optimization model. The control command generation unit is used to determine the control commands of the new energy power system based on the multi-period rolling quadratic programming model.

8. An electronic device, comprising: processor; as well as A memory storing a computer program that, when executed by the processor, causes the processor to perform the control method as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor, cause the processor to perform the control method as described in any one of claims 1-6.

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