Energy scheduling optimization method of power system and related equipment

Abstract

The invention provides an energy scheduling optimization method of a power system and related equipment. The method comprises the following steps: constructing a prediction model based on obtained historical load data, current load data and future load data of a target power system in multiple scheduling periods; inputting the historical load data and the current load data into a prediction model for prediction, obtaining a difference value between the future prediction data and the future load data, and correcting the prediction model to obtain a target prediction model; for each scheduling period, optimizing the control quantity through the target prediction model and the constructed system constraint condition to obtain an optimal control sequence; selecting a group of control quantities corresponding to the current scheduling period of the target power system from the optimal control sequence to schedule the target power system in real time to obtain a real-time scheduling optimization result of the target power system; compared with the prior art, the operation stability and the dispatching precision of the novel power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy dispatching of electric power systems, and in particular to an energy dispatching optimization method and related equipment for electric power systems. Background Art

[0002] With the rapid development of economy, the demand for electricity in human society is growing. In order to achieve the goal of green and low-carbon development, new power systems are being vigorously promoted. The key to developing new power systems lies in the stability and accuracy of system operation.

[0003] Most of the dispatching methods of traditional power systems only build statistical models through historical data, and then perform calculations through statistical models. However, with the gradual increase in the proportion of renewable energy such as wind power and solar energy in the power system, the dispatching methods of traditional power systems have obvious shortcomings in dealing with the intermittent and volatile nature of new energy. The increase in the proportion of new energy power generation has put forward higher requirements for the stable operation and dispatching optimization of the power system. Accurately predicting the power generation and load demand of new power systems has become the key to optimizing dispatching, but a single prediction model is difficult to fully cope with the uncertainty of new energy power generation, resulting in poor operating stability of the power system and low dispatching accuracy. Summary of the invention

[0004] The present invention provides an energy dispatch optimization method for an electric power system and related equipment, the purpose of which is to improve the operating stability and dispatch accuracy of a new type of electric power system.

[0005] In order to achieve the above object, the present invention provides an energy dispatch optimization method for a power system, comprising:

[0006] Step 1, obtaining historical load data, current load data, and future load data of the target power system under multiple dispatch cycles;

[0007] Step 2: construct a prediction model based on historical load data, current load data, and future load data;

[0008] Step 3: Input the historical load data and the current load data into the prediction model for prediction to obtain future prediction data, and use the difference between the future prediction data and the future load data to correct the prediction model to obtain the target prediction model;

[0009] Step 4: For each scheduling cycle, the control quantity is optimized through the target prediction model and the constructed system constraints to obtain the optimal control sequence. The optimal control sequence includes the control quantities corresponding to all scheduling cycles. Each group of control quantities includes energy scheduling instructions, valve opening adjustment, and power distribution.

[0010] Step 5, determine the current dispatch cycle of the target power system, select a set of control quantities corresponding to the current dispatch cycle of the target power system in the optimal control sequence, and dispatch the target power system in real time to obtain the real-time dispatch optimization result of the target power system.

[0011] More specifically, the prediction model includes:

[0012] The state space equations used to describe the dynamic behavior of the power are expressed as:

[0013]

[0014] Where x represents the state variable, x1, x2, and x3 represent the historical load data or the current load data or the future load data, respectively, u represents the input variable, p represents the control quantity of the target power system, represents the system parameter of the target power system, and f(·) represents a function used to describe the relationship between the state variable and the input variable and the system parameter;

[0015] The transfer function is used to establish the relationship between the historical load data of the target power system input, the current load data and the future load data of the system output. The expression is:

[0016]

[0017] Among them, Y(s) represents the Laplace transform result of the future load data output by the system, and U(s) represents the Laplace transform result of the historical load data and current load data input by the system;

[0018] The incremental model is used to convert the prediction model into an incremental form. The expression is:

[0019] Δx(k)=x(k)-x(k-1)

[0020] Δu(k)=u(k)-u(k-1)

[0021] Among them, Δx(k) represents the increment of the state variable at time k, Δu(k) represents the increment of the control quantity at time k, x(k) represents the state variable at time k, x(k-1) represents the state variable at time k-1, u(k) represents the control quantity at time k, and u(k-1) represents the control quantity at time k-1.

[0022] Specifically, system constraints include state variable constraints, control quantity constraints, system output constraints, and dynamic constraints.

[0023] Further, the state variable constraints include voltage constraints, current constraints, and power constraints;

[0024] The voltage constraint is expressed as:

[0025] V min ≤V(k)≤V max

[0026] The current constraint is expressed as:

[0027] I min ≤I(k)≤I max

[0028] The power constraint is expressed as:

[0029] P min ≤P(k)≤P max

[0030] Where V(k) represents the voltage value at time k, I(k) represents the current value at time k, P(k) represents the power value at time k, V min Indicates the minimum allowable voltage, V max Indicates the maximum allowable voltage, I min Indicates the maximum allowable current, I max Indicates the minimum allowable current, P min Indicates the maximum value of power allowed, P max Indicates the minimum value allowed for power.

[0031] Furthermore, the control quantity constraints include energy dispatch instruction constraints, valve opening adjustment constraints, and power allocation constraints;

[0032] Among them, the energy dispatch instruction constraint is expressed as:

[0033]

[0034] The valve opening adjustment constraint is expressed as:

[0035]

[0036] The power allocation constraint is expressed as:

[0037]

[0038] Among them, u1(k) represents the energy dispatch instruction value at time k, u2(k) represents the valve opening adjustment value at time k, and represents the power distribution value at time k. represents the minimum value of the energy dispatch instruction allowed at time k, represents the maximum value of the energy dispatch instruction allowed at time k, Indicates the minimum value of the valve opening adjustment value allowed at time k, Indicates the maximum value of the valve opening adjustment value allowed at time k, represents the minimum value of the power allocation value allowed at time k, Indicates the maximum value of the power allocation allowed at time k.

[0039] Specifically, the system output constraint is to constrain the tracking reference signal, which is expressed as:

[0040] y min (k)≤y(k)≤y max (k)

[0041] Among them, y(k) represents the tracking reference signal output at time k, y min (k) represents the minimum value of the tracking reference signal allowed at time k, y max (k) represents the maximum value of the tracking reference signal allowed at time k.

[0042] Furthermore, the dynamic constraint is to constrain the rate of change of the control variable, which can be expressed as:

[0043] Δu min ≤u(k)-u(k-1)≤Δu max

[0044] Among them, u(k)-u(k-1) represents the rate of change of the control quantity, Δu min Indicates the minimum rate of change allowed for the control variable, Δu max Indicates the maximum rate of change allowed for the controlled variable.

[0045] The present invention also provides an energy dispatch optimization device for an electric power system, comprising:

[0046] An acquisition module is used to acquire historical load data, current load data, and future load data of a target power system under multiple dispatch cycles;

[0047] A construction module for constructing a prediction model based on historical load data, current load data, and future load data;

[0048] A correction module is used to input historical load data and current load data into the prediction model for prediction, obtain future prediction data, and use the difference between the future prediction data and the future load data to correct the prediction model to obtain a target prediction model;

[0049] The optimization module is used to optimize the control quantity for each scheduling cycle through the target prediction model and the constructed system constraints to obtain the optimal control sequence. The optimal control sequence includes the control quantities corresponding to all scheduling cycles. Each group of control quantities includes energy scheduling instructions, valve opening adjustment, and power distribution.

[0050] The scheduling module is used to determine the current scheduling cycle of the target power system, select a set of control quantities corresponding to the current scheduling cycle of the target power system in the optimal control sequence, and schedule the target power system in real time to obtain the real-time scheduling optimization result of the target power system.

[0051] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements an energy dispatch optimization method for a power system when executing the computer program.

[0052] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for optimizing energy dispatching of a power system is implemented.

[0053] The above scheme of the present invention has the following beneficial effects:

[0054] The present invention constructs a prediction model based on the historical load data, current load data and future load data of the target power system in multiple dispatching cycles; inputs the historical load data and the current load data into the prediction model for prediction to obtain future prediction data, and uses the difference between the future prediction data and the future load data to correct the prediction model to obtain the target prediction model; for each dispatching cycle, the control quantity is optimized by the target prediction model and the constructed system constraints to obtain an optimal control sequence, and the optimal control sequence includes control quantities corresponding to all dispatching cycles, and each group of control quantities includes energy dispatching instructions, valve opening adjustment, and power distribution; determines the current dispatching cycle of the target power system, selects a group of control quantities corresponding to the current dispatching cycle of the target power system in the optimal control sequence, and dispatches the target power system in real time to obtain a real-time dispatch optimization result of the target power system; compared with the prior art, the present invention establishes a prediction model and system constraints, and uses the revised prediction model and system constraints to optimize the control quantity of the target power system and then dispatches the target power system, thereby improving the operation stability and dispatching accuracy of the new power system.

[0055] Other beneficial effects of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of a flow chart of an embodiment of the present invention;

[0057] Figure 2 It is a structural schematic diagram of an energy scheduling optimization device in an embodiment of the present invention;

[0058] Figure 3 Schematic diagram of the structure of a terminal device in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0061] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a locking connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0062] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0063] In view of the existing problems, the present invention provides an energy dispatch optimization method and related equipment for an electric power system.

[0064] like Figure 1 As shown, an embodiment of the present invention provides an energy dispatch optimization method for a power system, comprising:

[0065] Step 1, obtaining historical load data, current load data, and future load data of the target power system under multiple dispatch cycles;

[0066] Step 2: construct a prediction model based on historical load data, current load data and future prediction data;

[0067] Step 3: Input the historical load data and the current load data into the prediction model for prediction to obtain future prediction data, and use the difference between the future prediction data and the future load data to correct the prediction model to obtain the target prediction model;

[0068] Step 4: For each scheduling cycle, the control quantity is optimized through the target prediction model and the constructed system constraints to obtain the optimal control sequence. The optimal control sequence includes the control quantities corresponding to all scheduling cycles. Each group of control quantities includes energy scheduling instructions, valve opening adjustment, and power distribution.

[0069] Step 5, determine the current dispatch cycle of the target power system, select a set of control quantities corresponding to the current dispatch cycle of the target power system in the optimal control sequence, and dispatch the target power system in real time to obtain the real-time dispatch optimization result of the target power system.

[0070] In the embodiment of the present invention, the scheduling cycle is selected as every 24 hours, and the scheduling cycle can be dynamically adjusted according to the accuracy of load forecasting and the volatility of renewable energy to improve the responsiveness of scheduling.

[0071] In the embodiment of the present invention, the historical load data, the current load data and the future load data all include voltage, current and power, wherein the future load data is obtained by combining the historical load data and the weather forecast data, using time series analysis and state estimation technology, and the prediction model is determined according to the characteristics of the power system, and is used to describe the dynamic behavior of the target power system and predict the future prediction data within a period of time in the future. The future prediction data includes voltage data, current data and power data. The characteristics of the power system mainly include simultaneity, integrity, rapidity, continuity, real-time, randomness, etc. Specifically:

[0072] The simultaneity of the power system: power generation, transmission and consumption are completed at the same time, and large amounts of power cannot be stored;

[0073] Integrity: Power plants, transformers, high-voltage transmission lines, distribution lines and electrical equipment are an integral whole in the power grid;

[0074] Rapidity: The power transmission process is rapid;

[0075] Continuity: Electricity needs to be adjusted all the time;

[0076] Real-time: Power grid accidents occur quickly and involve a wide range of areas, requiring constant safety monitoring;

[0077] Randomness: Random changes in load, abnormal conditions and accidents during operation.

[0078] Most preferably, the prediction model comprises:

[0079] The state space equations used to describe the dynamic behavior of the power are expressed as:

[0080]

[0081] Where x represents the state variable, x1, x2, and x3 represent the historical load data, current load data, and future load data, respectively, u represents the input variable, p represents the control quantity of the target power system, represents the system parameters of the target power system, such as voltage, current, and power, represents the fixed characteristics of the power system, and f(· represents a function used to describe the relationship between the state variable and the input variable and the system parameter;

[0082] The embodiment of the present invention monitors the dynamic behavior of the target power system in real time through a state space method, thereby determining the dynamic changes of voltage, current and power, and displaying the changes of system parameters according to the dynamic changes;

[0083] The transfer function is used to describe the dynamic behavior of the system when the linearity is invariant, and to establish the relationship between the historical load data of the target power system input, the current load data and the future load data of the system output. The expression is:

[0084]

[0085] Among them, Y(s) represents the Laplace transform result of the future load data output by the system, and U(s) represents the Laplace transform result of the historical load data and current load data input by the system;

[0086] The embodiment of the present invention analyzes the frequency response, stability and other characteristics of the target circuit system through the transfer function to ensure that the power system can operate stably;

[0087] The incremental model is used to convert the prediction model into an incremental form. The expression is:

[0088] Δx(k)=x(k)-x(k-1)

[0089] Δu(k)=u(k)-u(k-1)

[0090] Among them, Δx(k) represents the increment of the state variable at time k, Δu(k) represents the increment of the control amount at time k, x(k) represents the state variable at time k, x(k-1) represents the state variable at time k-1, u(k) represents the control amount at time k, and u(k-1) represents the control amount at time k-1;

[0091] The embodiments of the present invention can intuitively capture the dynamic changes of the system by describing the changes of state variables and control inputs in the form of increments, so that dynamic changes can be controlled according to the change rate range based on the change rate constraints in the constraints, thereby improving the utilization rate of energy after scheduling and ensuring the stable operation of the power system.

[0092] In practical applications, due to model errors, interference and uncertainty, the predicted value may deviate from the actual value. Therefore, it is necessary to use the actual future load data to correct the prediction model to reduce the error, as described in step 3:

[0093] Input historical load data and current load data into the forecasting model for forecasting to obtain future forecast data;

[0094] Calculate the difference between future forecast data and future load data. The expression is:

[0095] E(t)=P pred (t)-P actual (t)

[0096] Among them, E(t) represents the prediction error at time t, P pred (t) represents the predicted data (predicted value) at time t, P actual (t) represents the actual data (true value) at time t;

[0097] This formula calculates the difference between the predicted value and the actual value at time t;

[0098] The specific process of correcting the prediction model based on the difference and obtaining the target prediction model is as follows:

[0099] The system uses the actual output y mcas (t) is consistent with the model prediction The error between them is used to update the model, and y is set mcas (t) is the future load data of the system output obtained by direct measurement, and is the future prediction data based on the prediction output of the model at time t, and е(t) is the error value at time t.

[0100] First, calculate the error between the predicted output and the actual output:

[0101]

[0102] Then, the error is used to update the parameters such as energy dispatching instructions, valve opening adjustment, power allocation strategy, etc. in the model to obtain the target prediction model, so as to reduce the error in the next control dispatch cycle;

[0103] In order to evaluate the overall prediction accuracy of the system, the mean square error (MSE) is used as a metric. The expression of the mean square error is:

[0104]

[0105] Where N represents the total number of time steps and MSE represents the mean squared difference between the predicted value and the actual value.

[0106] This feedback correction method can improve the robustness and adaptability of the prediction model, enabling it to better cope with the dynamic changes of the power system, and correct the prediction results by iterating the data of the prediction model to improve the accuracy of the prediction.

[0107] Most preferably, the system constraints include state variable constraints, control quantity constraints, system output constraints and dynamic constraints.

[0108] Most preferably, the state variable constraints include voltage constraints, current constraints, and power constraints;

[0109] The voltage constraint is expressed as:

[0110] V min ≤V(k)≤V max

[0111] The current constraint is expressed as:

[0112] I min ≤I(k)≤I max

[0113] The power constraint is expressed as:

[0114] P min ≤P(k)≤P max

[0115] Where V(k) represents the voltage value at time k, I(k) represents the current value at time k, P(k) represents the power value at time k, V min Indicates the minimum allowable voltage, V max Indicates the maximum allowable voltage, I min Indicates the maximum allowable current, I max Indicates the minimum allowable current, P min Indicates the maximum value of power allowed, P max Indicates the minimum value allowed for power.

[0116] The embodiments of the present invention constrain the maximum and minimum values ​​of voltage, current, power, etc. at a certain moment, thereby preventing state variables such as voltage, current, power, etc. from exceeding the variation range when energy is scheduled for grid connection, thereby improving the utilization rate of energy after scheduling and ensuring the stable operation of the power system.

[0117] Most preferably, the control quantity constraints include energy dispatch instruction constraints, valve opening adjustment constraints, and power allocation constraints;

[0118] Among them, the energy dispatch instruction constraint is expressed as:

[0119]

[0120] The valve opening adjustment constraint is expressed as:

[0121]

[0122] The power allocation constraint is expressed as:

[0123]

[0124] Among them, u1(k) represents the energy dispatch instruction value at time k, u2(k) represents the valve opening adjustment value at time k, and represents the power distribution value at time k. represents the minimum value of the energy dispatch instruction allowed at time k, represents the maximum value of the energy dispatch instruction allowed at time k, Indicates the minimum value of the valve opening adjustment value allowed at time k, Indicates the maximum value of the valve opening adjustment value allowed at time k, represents the minimum value of the power allocation value allowed at time k, Indicates the maximum value of the power allocation allowed at time k.

[0125] The embodiments of the present invention can improve the control accuracy of new energy scheduling and grid connection by limiting the range of energy scheduling instructions and valve opening adjustment, thereby narrowing the spatial range of new energy scheduling, thereby enabling more reasonable energy scheduling and optimization, avoiding waste, and reducing carbon emissions.

[0126] Most preferably, the system output constraint is to constrain the tracking reference signal, expressed as:

[0127] y min (k)≤y(k)≤y max (k)

[0128] Among them, y(k) represents the tracking reference signal output at time k, y min (k) represents the minimum value of the tracking reference signal allowed at time k, y max (k) represents the maximum value of the tracking reference signal allowed at time k.

[0129] The embodiments of the present invention can more accurately ensure the stability of the signal by constraining the tracking reference signal in the power system, thereby reducing signal fluctuations and avoiding communication delays, so that the control strategy can be communicated more quickly and the accuracy of optimizing the grid connection of new energy sources in the power system can be improved.

[0130] Most preferably, the dynamic constraint is to constrain the rate of change of the control variable, which is expressed as:

[0131] Δu min ≤u(k)-u(k-1)≤Δu max

[0132] Among them, u(k)-u(k-1) represents the rate of change of the control quantity, Δu min Indicates the minimum rate of change allowed for the control variable, Δu max Indicates the maximum rate of change allowed for the controlled variable.

[0133] The embodiment of the present invention constrains the input change rate of energy in the power system to avoid insufficient power supply due to insufficient input and excessive energy input that causes load on the power system and reduces utilization, thereby improving energy utilization through reasonable constraint control and improving stability in the energy scheduling process.

[0134] The embodiment of the present invention builds a prediction model based on the historical load data, current load data and future load data of the target power system in multiple dispatching cycles; inputs the historical load data and the current load data into the prediction model for prediction to obtain future prediction data, and uses the difference between the future prediction data and the future load data to correct the prediction model to obtain the target prediction model; for each dispatching cycle, the control amount is optimized by the target prediction model and the constructed system constraints to obtain an optimal control sequence, and the optimal control sequence includes control amounts corresponding to all dispatching cycles, and each group of control amounts includes energy dispatching instructions, valve opening adjustment, and power distribution; determines the current dispatching cycle of the target power system, selects a group of control amounts corresponding to the current dispatching cycle of the target power system in the optimal control sequence, and dispatches the target power system in real time to obtain a real-time dispatch optimization result of the target power system; compared with the prior art, the embodiment of the present invention establishes a prediction model and system constraints, and uses the revised prediction model and system constraints to optimize the control amount of the target power system and then dispatches the target power system, thereby improving the operation stability and dispatching accuracy of the new power system.

[0135] The present invention also provides an energy dispatch optimization device for a power system, such as Figure 2 As shown, the energy scheduling optimization device 100 includes:

[0136] An acquisition module 101 is used to acquire historical load data, current load data, and future load data of a target power system under multiple dispatch cycles;

[0137] A construction module 102 is used to construct a prediction model based on historical load data, current load data and future prediction data;

[0138] The correction module 103 is used to input the historical load data and the current load data into the prediction model for prediction to obtain future prediction data, and to correct the prediction model using the difference between the future prediction data and the future load data to obtain a target prediction model;

[0139] The optimization module 104 is used to optimize the control quantity for each scheduling cycle through the target prediction model and the constructed system constraints to obtain the optimal control sequence. The optimal control sequence includes the control quantities corresponding to all scheduling cycles. Each group of control quantities includes energy scheduling instructions, valve opening adjustment, and power distribution.

[0140] The scheduling module 105 is used to determine the current scheduling period of the target power system, select a set of control quantities corresponding to the current scheduling period of the target power system in the optimal control sequence, and schedule the target power system in real time to obtain a real-time scheduling optimization result of the target power system.

[0141] In some embodiments, the predictive model includes:

[0142] The state space equations used to describe the dynamic behavior of the power are expressed as:

[0143]

[0144] Where x represents the state variable, x1, x2, and x3 represent the historical load data, current load data, and future load data, respectively, u represents the input variable, p represents the control quantity of the target power system, represents the system parameters of the target power system, such as voltage, current, and power, and represents the fixed characteristics of the power system, and f(·) represents a function used to describe the relationship between the state variable and the input variable and system parameters;

[0145] The transfer function is used to describe the dynamic behavior of the system when the linearity is invariant, and to establish the relationship between the historical load data of the target power system input, the current load data and the future load data of the system output. The expression is:

[0146]

[0147] Among them, Y(s) represents the Laplace transform result of the future load data output by the system, and U(s) represents the Laplace transform result of the historical load data and current load data input by the system;

[0148] The incremental model is used to convert the prediction model into an incremental form. The expression is:

[0149] Δx(k)=x(k)-x(k-1)

[0150] Δu(k)=u(k)-u(k-1)

[0151] Among them, Δx(k) represents the increment of the state variable at time k, Δu(k) represents the increment of the control quantity at time k, x(k) represents the state variable at time k, x(k-1) represents the state variable at time k-1, u(k) represents the control quantity at time k, and u(k-1) represents the control quantity at time k-1.

[0152] In some embodiments, system constraints include state variable constraints, control quantity constraints, system output constraints, and dynamic constraints.

[0153] In some embodiments, the state variable constraints include voltage constraints, current constraints, and power constraints;

[0154] The voltage constraint is expressed as:

[0155] V min ≤V(k)≤V max

[0156] The current constraint is expressed as:

[0157] I min ≤I(k)≤I max

[0158] The power constraint is expressed as:

[0159] P min ≤P(k)≤P max

[0160] Where V(k) represents the voltage value at time k, I(k) represents the current value at time k, P(k) represents the power value at time k, V min Indicates the minimum allowable voltage, V max Indicates the maximum allowable voltage, I min Indicates the maximum allowable current, I max Indicates the minimum allowable current, P min Indicates the maximum value of power allowed, P max Indicates the minimum value allowed for power.

[0161] In some embodiments, the control quantity constraints include energy dispatch instruction constraints, valve opening adjustment constraints, and power allocation constraints;

[0162] Among them, the energy dispatch instruction constraint is expressed as:

[0163]

[0164] The valve opening adjustment constraint is expressed as:

[0165]

[0166] Among them, u1(k) represents the energy dispatch instruction at time k, u2(k) represents the valve opening adjustment value at time k, represents the minimum value of the energy dispatch instruction allowed at time k, represents the maximum value of the energy dispatch instruction allowed at time k, Indicates the minimum value of the valve opening adjustment value allowed at time k, Indicates the maximum value of the valve opening adjustment value allowed at time k.

[0167] In some embodiments, the system output constraint is to constrain the tracking reference signal, which is expressed as:

[0168] y min (k)≤y(k)≤y max (k)

[0169] Among them, y(k) represents the tracking reference signal output at time k, y min (k) represents the minimum value of the tracking reference signal allowed at time k, y max (k) represents the maximum value of the tracking reference signal allowed at time k.

[0170] In some embodiments, the dynamic constraint is a constraint on the rate of change of the control variable, which is expressed as:

[0171] Δu min ≤u(k)-u(k-1)≤Δu max

[0172] Among them, u(k)-u(k-1) represents the rate of change of the control quantity, Δu min Indicates the minimum rate of change allowed for the control variable, Δu max Indicates the maximum rate of change allowed for the controlled variable.

[0173] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0174] like Figure 3 As shown, the present invention also provides a terminal device D10, including at least one processor D100 ( Figure 3 Only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 implements the energy dispatch optimization method of the power system when executing the computer program D102. Alternatively, the processor D100 implements the functions of each module / unit in the above-mentioned device embodiments when executing the computer program D102.

[0175] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0176] In some embodiments, the memory may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. In other embodiments, the memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (SMC, Smart Media Card), a secure digital (SD, Secure Digital) card, a flash card (Flash Card), etc. equipped on the terminal device. Further, the memory may also include both an internal storage unit of the terminal device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (Boot Loader), data and other programs, such as the program code of the computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0177] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0178] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0179] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for optimizing energy dispatching of a power system is implemented.

[0180] If the integrated module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above method embodiment, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the construction device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0181] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing energy dispatching of a power system, characterized in that: include: Step 1, obtaining historical load data, current load data, and future load data of the target power system under multiple dispatch cycles; Step 2: constructing a prediction model based on the historical load data, the current load data and the future load data; Step 3, inputting the historical load data and the current load data into the prediction model for prediction to obtain future prediction data, and using the difference between the future prediction data and the future load data to correct the prediction model to obtain a target prediction model; Step 4: For each scheduling cycle, the control amount is optimized through the target prediction model and the constructed system constraints to obtain an optimal control sequence, wherein the optimal control sequence includes control amounts corresponding to all scheduling cycles, and each group of control amounts includes energy scheduling instructions, valve opening adjustment, and power distribution; Step 5, determine the current dispatch cycle of the target power system, select a set of control quantities corresponding to the current dispatch cycle of the target power system in the optimal control sequence, and dispatch the target power system in real time to obtain a real-time dispatch optimization result of the target power system.

2. The energy dispatch optimization method of the power system according to claim 1, characterized in that: The prediction model includes: The state space equations used to describe the dynamic behavior of the power are expressed as: Where x represents the state variable, x1, x2, and x3 represent the historical load data or the current load data or the future load data, respectively, u represents the input variable, p represents the control quantity of the target power system, represents the system parameter of the target power system, and f(·) represents a function used to describe the relationship between the state variable and the input variable and the system parameter; The transfer function is used to establish the relationship between the historical load data, current load data and future load data of the system data input by the target power system, and the expression is: Among them, Y(s) represents the Laplace transform result of the future load data output by the system, and U(s) represents the Laplace transform result of the historical load data and current load data input by the system; The incremental model is used to convert the prediction model into an incremental form, and the expression is: Δx(k)=x(k)-x(k-1) Δu(k)=u(k)-u(k-1) Among them, Δx(k) represents the increment of the state variable at time k, Δu(k) represents the increment of the control quantity at time k, x(k) represents the state variable at time k, x(k-1) represents the state variable at time k-1, u(k) represents the control quantity at time k, and u(k-1) represents the control quantity at time k-1.

3. The energy dispatch optimization method of the power system according to claim 2 is characterized in that: The system constraints include state variable constraints, control quantity constraints, system output constraints and dynamic constraints.

4. The method for optimizing energy dispatching of a power system according to claim 3, characterized in that: The state variable constraints include voltage constraints, current constraints and power constraints; The voltage constraint is expressed as: V min ≤V(k)≤V max The current constraint is expressed as: I min ≤I(k)≤I max The power constraint is expressed as: P min ≤P(k)≤P max Where V(k) represents the voltage value at time k, I(k) represents the current value at time k, P(k) represents the power value at time k, V min Indicates the minimum allowable voltage, V max Indicates the maximum allowable voltage, I min Indicates the maximum allowable current, I max Indicates the minimum allowable current, P min Indicates the maximum value of power allowed, P max Indicates the minimum value allowed for power.

5. The method for optimizing energy dispatching of a power system according to claim 3, characterized in that: The control quantity constraints include energy dispatch instruction constraints, valve opening adjustment constraints, and power allocation constraints; Among them, the energy dispatch instruction constraint is expressed as: The valve opening adjustment constraint is expressed as: The power allocation constraint is expressed as: Among them, u1(k) represents the energy dispatch instruction value at time k, u2(k) represents the valve opening adjustment value at time k, and represents the power distribution value at time k. represents the minimum value of the energy dispatch instruction allowed at time k, represents the maximum value of the energy dispatch instruction allowed at time k, Indicates the minimum value of the valve opening adjustment value allowed at time k, Indicates the maximum value of the valve opening adjustment value allowed at time k, represents the minimum value of the power allocation value allowed at time k, Indicates the maximum value of the power allocation allowed at time k.

6. The method for optimizing energy dispatching of a power system according to claim 3, characterized in that: The system output constraint is to constrain the tracking reference signal, which is expressed as: y min (k)≤y(k)≤y max (k) Among them, y(k) represents the tracking reference signal output at time k, y min (k) represents the minimum value of the tracking reference signal allowed at time k, y max (k) represents the maximum value of the tracking reference signal allowed at time k.

7. The method for optimizing energy dispatching of a power system according to claim 6, characterized in that: The dynamic constraint is to constrain the change rate of the control variable, which is expressed as: Δu min ≤u(k)-u(k-1)≤Δu max Among them, u(k)-u(k-1) represents the rate of change of the control quantity, Δu min Indicates the minimum rate of change allowed for the control variable, Δu max Indicates the maximum rate of change allowed for the controlled variable.

8. An energy dispatch optimization device for a power system, characterized in that: include: An acquisition module is used to acquire historical load data, current load data, and future load data of a target power system under multiple dispatch cycles; A construction module, configured to construct a prediction model based on the historical load data, the current load data and the future prediction data; A correction module, used for inputting the historical load data and the current load data into the prediction model for prediction to obtain future prediction data, and correcting the prediction model using the difference between the future prediction data and the future load data to obtain a target prediction model; An optimization module is used to optimize the control quantity for each scheduling cycle through the target prediction model and the constructed system constraints to obtain an optimal control sequence, wherein the optimal control sequence includes control quantities corresponding to all scheduling cycles, and each group of control quantities includes energy scheduling instructions, valve opening adjustment, and power distribution; The scheduling module is used to determine the current scheduling period of the target power system, select a group of control quantities corresponding to the current scheduling period of the target power system in the optimal control sequence, and schedule the target power system in real time to obtain the real-time scheduling optimization result of the target power system.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the energy dispatch optimization method of the power system as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for optimizing energy dispatch of a power system as described in any one of claims 1 to 7 is implemented.

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