On-line electric energy dispatching and adjusting method and system for electric power system

Abstract

The present invention provides an online power dispatch adjustment method and system for a power system, wherein the method includes: determining online demand adjustment constraints; wherein the online demand adjustment constraints include: demand adjustment constraints, real-time market power generation constraints, and ramp power generation constraints; establishing an online demand adjustment model based on the constraints; obtaining input values for the online demand adjustment model, and inputting the input values into the online demand adjustment model to obtain an online demand adjustment result; wherein the input values include: the adjustment size of the user's power demand and the adjustable range of the user's power demand. The present invention considers user demand adjustment as an online optimization problem, improves the efficiency of the entire system, and is more consistent with actual settings.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an online electric energy dispatching and adjusting method and system for a power system. Background Art

[0002] In a microgrid system, each participant acts as a prosumer. When the required electricity is insufficient, each participant can generate electricity on an emergency basis; when there is excess electricity, the participant can sell the electricity.

[0003] In the existing technologies, the behavior scheduling of each participant is mainly offline scheduling. For market participant i, at time t, its actual and predicted electricity demand are D i,t and And the prediction error e i,t express:

[0004]

[0005] The cost of market participant i consists of two parts: one is the cost of electricity generated in the day-ahead market, and the other is the cost of handling electricity imbalance in the real-time market. The cost of the real-time market corresponds to two situations: when the electricity generated in the day-ahead market is lower than the electricity demand, participant i adjusts his electricity demand to x i,t And emergency power generation; when the electricity market production is higher than the electricity demand, participant i sells the excess electricity. Therefore, the cost function C i,t (x i,t ) is defined as:

[0006]

[0007] Among them, π t represents the cost of electricity generated on the day before, π s and π e They correspond to the real-time market unit shortage penalty and excess penalty respectively.

[0008] When considering electricity elasticity adjustment, existing technologies generally only consider offline scheduling, and the calculation results are inaccurate and far from the actual situation. Summary of the Invention

[0009] To solve the above problems in the prior art, the present invention provides an online power dispatching and adjustment method and system for a power system, which considers the user's demand adjustment as an online optimization problem, improves the efficiency of the entire system, and is more in line with the actual setting.

[0010] A first aspect of the present invention provides an online electric energy dispatching and adjustment method for an electric power system, comprising:

[0011] Determining online demand adjustment constraints; wherein the online demand adjustment constraints include: demand adjustment constraints, real-time market power generation constraints, and ramp power generation constraints;

[0012] establishing an online demand adjustment model according to the constraint conditions;

[0013] The input value of the online demand adjustment model is obtained, and the input value is input into the online demand adjustment model to obtain the online demand adjustment result; wherein the input value includes: the user power demand adjustment size and the user power demand adjustable range.

[0014] Furthermore, the constraint function of the demand adjustment constraint is expressed by the following formula:

[0015] g 1，i，t (x i，t )= α i D i，t -x i，t ≤0;

[0016]

[0017] Among them, x i,t is the electricity demand, g 1,i,t (x i,t ) and g 2,i,t (x i,t ) are all constraint functions of demand adjustment constraints, α i is the demand-adjusted lower bound, is the upper bound of demand adjustment, D i,t The actual electricity demand.

[0018] Furthermore, the constraint function of the real-time market power generation constraint is expressed by the following formula:

[0019]

[0020] Among them, x i,t is the electricity demand, g 3,i,t (x i,t ) is the constraint function of the real-time market power generation constraint, To predict electricity demand, is the upper limit of power generation, E represents the expected cost function, {} + Indicates that the result is positive.

[0021] Furthermore, the constraint function of the ramp power generation constraint is expressed by the following formula:

[0022]

[0023]

[0024] Among them, x i，t and x i，t-1 is the electricity demand at two consecutive sampling times t-1 and t, g 4，i，t (x i，t , x i，t-1 ) and g 5，i，t (x i，t , x i，t-1 ) are all constraint functions for ramp power generation constraints, and is the predicted electricity demand at two consecutive sampling times t-1 and t, is the upper limit of the climbing constraint, r i is the lower limit of the climbing constraint, E represents the expected cost function, {} + Indicates that the result is positive.

[0025] Furthermore, the online demand adjustment model is expressed by the following formula:

[0026]

[0027]

[0028]

[0029] g j，i，t (x)≤0, j∈{1, 2, 3}, t∈{1,...,T};

[0030]

[0031] x i，t ≥0, t∈{1,...,T};

[0032] x≥0;

[0033]

[0034] Among them, x i，kT+t is the electricity demand at sampling time kT+t, is the adjustable range of user power demand, T is the maximum sampling time, x i，t is the electricity demand, C i，t (x i，t ) is the cost function, E represents the expected cost function, {} + Indicates that the result is positive, g j，i，t (x i，t ) is the constraint function, g 1，i，t (x i , t ) and g 2，i，t (xi , t ) are both constraint functions of demand adjustment constraints, g 3，i，t (x i，t ) is the constraint function of real-time market power generation constraint, g 4，i，t (x i，t , x i，t-1 ) and g 5，i，t (x i，t , x i，t-1 ) are all constraint functions of ramp power generation constraints, x is the actual power demand, To predict electricity demand, R is the maximum value of the adjustable range of electricity demand.

[0035] A second aspect of the present invention provides an online electric energy dispatching and adjustment system for an electric power system, comprising:

[0036] A constraint condition confirmation module is used to determine online demand adjustment constraint conditions; wherein the online demand adjustment constraint conditions include: demand adjustment constraint, real-time market power generation constraint and ramp power generation constraint;

[0037] An online demand adjustment model establishment module, used to establish an online demand adjustment model according to the constraint conditions;

[0038] The online demand adjustment model calculation module is used to obtain the input value of the online demand adjustment model and input the input value into the online demand adjustment model to obtain the online demand adjustment result; wherein, the input value includes: the user electricity demand adjustment size and the adjustable range of the user electricity demand.

[0039] Furthermore, the constraint function of the demand adjustment constraint is expressed by the following formula:

[0040] g 1，i，t (x i，t )= α i D i，t -x i，t ≤0;

[0041]

[0042] Among them, x i,t is the electricity demand, g 1,i,t (x i,t ) and g 2,i,t (x i,t ) are all constraint functions of demand adjustment constraints, α i is the demand-adjusted lower bound, is the upper bound of demand adjustment, D i,t The actual electricity demand.

[0043] Furthermore, the constraint function of the real-time market power generation constraint is expressed by the following formula:

[0044]

[0045] Among them, x i,t is the electricity demand, g 3,i,t (x i,t ) is the constraint function of the real-time market power generation constraint, To predict electricity demand, is the upper limit of power generation, E represents the expected cost function, {} + Indicates that the result is positive.

[0046] Furthermore, the constraint function of the ramp power generation constraint is expressed by the following formula:

[0047]

[0048]

[0049] Among them, x i，t and x i，t-1 is the electricity demand at two consecutive sampling times t-1 and t, g 4，i，t (x i，t , x i，t-1 ) and g 5，i，t (x i，t , x i，t-1 ) are all constraint functions for ramp power generation constraints, and is the predicted electricity demand at two consecutive sampling times t-1 and t, is the upper limit of the climbing constraint, r i is the lower limit of the climbing constraint, E represents the expected cost function, {} + Indicates that the result is positive.

[0050] Furthermore, the online demand adjustment model is expressed by the following formula:

[0051]

[0052]

[0053]

[0054] g j，i，t (x)≤0, j∈{1, 2, 3}, t∈{1,...,T};

[0055]

[0056] xi，t ≥0, t∈{1,...,T};

[0057] x≥0;

[0058]

[0059] Among them, x i，kT+t is the electricity demand at sampling time kT+t, is the adjustable range of user power demand, T is the maximum sampling time, x i，t is the electricity demand, C i，t (x i，t ) is the cost function, E represents the expected cost function, {} + Indicates that the result is positive, g j，i，t (x i，t ) is the constraint function, g 1，i，t (x i，t ) and g 2，i，t (x i，t ) are both constraint functions of demand adjustment constraints, g 3，i，t (x i，t ) is the constraint function of real-time market power generation constraint, g 4，i，t (x i，t , x i，t-1 ) and g 5，i，t (x i，t , x i，t-1 ) are all constraint functions of ramp power generation constraints, x is the actual power demand, To predict electricity demand, R is the maximum value of the adjustable range of electricity demand.

[0060] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0061] The present invention provides an online electric energy dispatching and adjustment method and system for an electric power system, wherein the method includes: determining online demand adjustment constraints; wherein the online demand adjustment constraints include: demand adjustment constraints, real-time market power generation constraints, and ramp power generation constraints; establishing an online demand adjustment model based on the constraints; obtaining input values of the online demand adjustment model, and inputting the input values into the online demand adjustment model to obtain online demand adjustment results; wherein the input values include: the adjustment size of the user's power demand and the adjustable range of the user's power demand. The present invention considers the user's demand adjustment as an online optimization problem, improves the efficiency of the entire system, and is more in line with the actual settings; applies the existing online optimization algorithm to the online demand adjustment problem, and improves it on the basis of the original algorithm, taking into account the effects of different constraint equation parameters at each moment, reducing the search space range, and considering the relationship between two moments in the constraint function, which is more in line with the actual situation of our problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 This is a flow chart of an online electric energy dispatching and adjustment method for a power system provided by a certain embodiment of the present invention;

[0064] Figure 2 This is a device diagram of an online electric energy dispatching and adjustment system for a power system provided by one embodiment of the present invention;

[0065] Figure 3 This is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0067] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0068] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0069] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0070] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0071] The first aspect.

[0072] See also Figure 1A certain embodiment of the present invention provides an online electric energy dispatching and adjusting method for an electric power system, comprising:

[0073] S10. Determine online demand adjustment constraint conditions; wherein the online demand adjustment constraint conditions include: demand adjustment constraint, real-time market power generation constraint, and ramp power generation constraint.

[0074] S20: Establish an online demand adjustment model according to the constraint conditions.

[0075] S30. Obtain input values of an online demand adjustment model, and input the input values into the online demand adjustment model to obtain online demand adjustment results; wherein the input values include: the adjustment size of the user's electricity demand and the adjustable range of the user's electricity demand.

[0076] Preferably, the constraint function of the demand adjustment constraint is expressed by the following formula:

[0077] g 1，i，t (x i，t )= α i D i，t -x i，t ≤0;

[0078]

[0079] Among them, x i,t is the electricity demand, g 1,i,t (x i,t ) and g 2,i,t (x i,t ) are all constraint functions of demand adjustment constraints, α i is the demand-adjusted lower bound, is the upper bound of demand adjustment, D i,t The actual electricity demand.

[0080] Preferably, the constraint function of the real-time market electricity production constraint is expressed by the following formula:

[0081]

[0082] Among them, x i，t is the electricity demand, g 3，i，t (x i，t ) is the constraint function of the real-time market power generation constraint, To predict electricity demand, is the upper limit of power generation, E represents the expected cost function, {} + Indicates that the result is positive.

[0083] Preferably, the constraint function of the ramp power generation constraint is expressed by the following formula:

[0084]

[0085]

[0086] Among them, x i，t and x i，t-1 is the electricity demand at two consecutive sampling times t-1 and t, g 4，i，t (x i，t , x i，t-1 ) and g 5，i，t (x i，t , x i，t-1 ) are all constraint functions for ramp power generation constraints, and is the predicted electricity demand at two consecutive sampling times t-1 and t, is the upper limit of the climbing constraint, r i is the lower limit of the climbing constraint, E represents the expected cost function, {} + Indicates that the result is positive.

[0087] Preferably, the online demand adjustment model is expressed by the following formula:

[0088]

[0089]

[0090]

[0091] g j，i，t (x)≤0, j∈{1, 2, 3}, t∈{1,…,T};

[0092]

[0093] x i，t 0, t∈{1,...,T};

[0094] x≥0;

[0095]

[0096] Among them, x i，kT+t is the electricity demand at sampling time kT+t, is the adjustable range of user power demand, T is the maximum sampling time, x i，t is the electricity demand, C i，t (x i，t ) is the cost function, E represents the expected cost function, {} +Indicates that the result is positive, g j，i，t (x i，t ) is the constraint function, g 1，i，t (x i，t ) and g 2，i，t (x i，t ) are both constraint functions of demand adjustment constraints, g 3，i，t (x i，t ) is the constraint function of real-time market power generation constraint, g 4，i，t (x i，t , x i，t-1 ) and g 5，i，t (x i，t , x i，t-1 ) are all constraint functions of ramp power generation constraints, x is the actual power demand, To predict electricity demand, R is the maximum value of the adjustable range of electricity demand.

[0097] Preferably, obtaining an input value of an online demand adjustment model and inputting the input value into the online demand adjustment model to obtain an online demand adjustment result includes:

[0098] In order to design the online power dispatch algorithm and analyze the algorithm, we analyze the above-mentioned optimization problem constructed.

[0099] Proposition 1: The expected cost function E{C i，t (·)} is convex and Lipschitz continuous.

[0100] Proposition 2: Constraint function g for online power dispatch optimization problem j，i，t (·), j∈{1, 2, 3, 4, 5} is Lipschitz continuous.

[0101]

[0102]

[0103] There are three assumptions about the parameters in the algorithm:

[0104] Assumption 1: The parameter R satisfies the following constraints,

[0105]

[0106] in, and D i are the maximum and minimum electricity demands of market participant i in the total time T, Determined by the distribution of forecast errors,

[0107]

[0108] Assumption 2: The parameter η satisfies the following constraints:

[0109]

[0110] in, And J satisfies,

[0111]

[0112] Assumption 3: The parameter δ satisfies the following constraints:

[0113] δ≥6G 2 +10δ 2 η 2 ;

[0114] Intuitively, the relationship between these three assumptions is easy to understand. When G increases, both R and η decrease. This means that when the search space When decreases, the learning step size η also decreases.

[0115] Theorem 1: When the parameters in the above algorithm satisfy assumptions 1, 2, and 3, the regret value constraint of the algorithm is

[0116] The method provided by the present invention considers the user demand adjustment as an online optimization problem, improves the efficiency of the entire system, and is more in line with the actual setting; applies the existing online optimization algorithm to the online demand adjustment problem, and improves it on the basis of the original algorithm, taking into account the different constraint equation parameters at each moment, the reduction of the search space range, and the constraint function considering the relationship between two moments, which is more in line with the actual situation of our problem.

[0117] The second aspect.

[0118] See also Figure 2 An embodiment of the present invention provides an online electric energy dispatching and adjustment system for an electric power system, comprising:

[0119] The constraint condition confirmation module 10 is used to determine the online demand adjustment constraint conditions; wherein the online demand adjustment constraint conditions include: demand adjustment constraint, real-time market power generation constraint and ramp power generation constraint;

[0120] An online demand adjustment model establishing module 20, configured to establish an online demand adjustment model according to the constraint conditions;

[0121] The online demand adjustment model calculation module 30 is used to obtain the input value of the online demand adjustment model and input the input value into the online demand adjustment model to obtain the online demand adjustment result; wherein the input value includes: the user electricity demand adjustment size and the adjustable range of the user electricity demand.

[0122] Preferably, the constraint function of the demand adjustment constraint is expressed by the following formula:

[0123] g 1，i，t (x i，t )= α i D i，t -x i，t ≤0;

[0124]

[0125] Among them, x i,t is the electricity demand, g 1,i,t (x i,t ) and g 2,i,t (x i,t ) are all constraint functions of demand adjustment constraints, α i is the demand-adjusted lower bound, is the upper bound of demand adjustment, D i,t The actual electricity demand.

[0126] Preferably, the constraint function of the real-time market electricity production constraint is expressed by the following formula:

[0127]

[0128] Among them, x i，t is the electricity demand, g 3，i，t (x i，t ) is the constraint function of the real-time market power generation constraint, To predict electricity demand, is the upper limit of power generation, E represents the expected cost function, {} + Indicates that the result is positive.

[0129] Preferably, the constraint function of the ramp power generation constraint is expressed by the following formula:

[0130]

[0131]

[0132] Among them, x i，t and x i，t-1 is the electricity demand at two consecutive sampling times t-1 and t, g 4，i，t (x i，t , x i，t-1 ) and g 5，i，t (x i，t , x i，t-1 ) are all constraint functions for ramp power generation constraints, and is the predicted electricity demand at two consecutive sampling times t-1 and t, is the upper limit of the climbing constraint, r i is the lower limit of the climbing constraint, E represents the expected cost function, {} + Indicates that the result is positive.

[0133] Preferably, the online demand adjustment model is expressed by the following formula:

[0134]

[0135]

[0136]

[0137] g j，i，t (x)≤0, j∈{1, 2, 3}, t∈{1,…,T};

[0138]

[0139] x i，t ≥0, t∈{1,...,T};

[0140] x≥0;

[0141]

[0142] Among them, x i，kT+t is the electricity demand at sampling time kT+t, is the adjustable range of user power demand, T is the maximum sampling time, x i，t is the electricity demand, C i，t (x i，t ) is the cost function, E represents the expected cost function, {} + Indicates that the result is positive, g j，i，t (x i，t ) is the constraint function, g 1，i，t (x i，t ) and g 2，i，t (x i，t ) are both constraint functions of demand adjustment constraints, g 3，i，t (x i，t ) is the constraint function of real-time market power generation constraint, g 4，i，t (x i，t , x i，t -1) and g 5，i，t (x i，t , x i，t -1) are all constraint functions for ramp power generation constraints, x is the actual power demand, To predict electricity demand, R is the maximum value of the adjustable range of electricity demand.

[0143] The system provided by the present invention considers the user's demand adjustment as an online optimization problem, improves the efficiency of the entire system, and is more in line with the actual setting; applies the existing online optimization algorithm to the online demand adjustment problem, and improves it on the basis of the original algorithm, taking into account the different constraint equation parameters at each moment, the reduction of the search space range, and the constraint function considering the relationship between two moments, which is more in line with the actual situation of our problem.

[0144] The third aspect.

[0145] The present invention provides an electronic device, comprising:

[0146] processor, memory, and bus;

[0147] The bus is used to connect the processor and the memory;

[0148] The memory is used to store operation instructions;

[0149] The processor is used to call the operation instruction, and the executable instruction enables the processor to perform operations corresponding to the online power scheduling and adjustment method of the power system as shown in the first aspect of the present application.

[0150] In an alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The electronic device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, for example, via a bus 5002. Optionally, the electronic device 5000 may further include a transceiver 5004. It should be noted that in actual applications, the number of transceivers 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation on the embodiments of the present application.

[0151] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0152] The bus 5002 may include a path for transmitting information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0153] The memory 5003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0154] The memory 5003 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0155] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0156] The fourth aspect.

[0157] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for online electric energy dispatching and adjusting an electric power system shown in the first aspect of the present application is implemented.

[0158] Another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiments.

Claims

1. A method for online power dispatching and adjusting a power system, characterized in that: include: Determining online demand adjustment constraints; wherein the online demand adjustment constraints include: demand adjustment constraints, real-time market power generation constraints, and ramp power generation constraints; establishing an online demand adjustment model according to the constraint conditions; Obtaining input values of an online demand adjustment model and inputting the input values into the online demand adjustment model to obtain an online demand adjustment result; wherein the input values include: the adjustment size of the user's electricity demand and the adjustable range of the user's electricity demand; The constraint function of the demand adjustment constraint is expressed by the following formula: g 1,i,t (x i,t )= α i D i,t -x i,t ≤0; Among them, x i,t is the electricity demand, g 1,i,t (x i,t ) and g 2,i,t (x i,t ) are all constraint functions of demand adjustment constraints, α i is the demand-adjusted lower bound, is the upper bound of demand adjustment, D i,t Actual electricity demand; The online demand adjustment model is expressed by the following formula: Among them, x i,kT+t is the electricity demand at sampling time kT+t, is the adjustable range of user power demand, T is the maximum sampling time, x i,t is the electricity demand, C i,t (x i,t ) is the cost function, E represents the expected cost function, {} + Indicates that the result is positive, g j,i,t (x i,t ) is the constraint function, g 1,i,t (x i,t ) and g 2,i,t (x i,t ) are both constraint functions of demand adjustment constraints, g 3,i,t (x i,t ) is the constraint function of real-time market power generation constraint, g 4,i,t (x i,t ,x i,t-1 ) and g 5,i,t (x i,t ,x i,t-1 ) are all constraint functions of ramp power generation constraints, x is the actual power demand, To predict electricity demand, R is the maximum value of the adjustable range of electricity demand; Among them, π t represents the cost of electricity generated on the day before, π s and π e They correspond to the real-time market unit shortage penalty and excess penalty, To predict electricity demand.

2. The online power dispatching and adjusting method of a power system according to claim 1, characterized in that: The constraint function of the real-time market power generation constraint is expressed by the following formula: Among them, x i,t is the electricity demand, g 3,i,t (x i,t ) is the constraint function of the real-time market power generation constraint, To predict electricity demand, is the upper limit of power generation, E represents the expected cost function, {} + Indicates that the result is positive.

3. The online power dispatching and adjusting method of a power system according to claim 1, characterized in that: The constraint function of the ramp power generation constraint is expressed by the following formula: Among them, x i,t and x i,t-1 is the electricity demand at two consecutive sampling times t-1 and t, g 4,i,t (x i,t ,x i,t-1 ) and g 5,i,t (x i,t ,x i,t-1 ) are all constraint functions for ramp power generation constraints, and is the predicted electricity demand at two consecutive sampling times t-1 and t, is the upper limit of the climbing constraint, r i is the lower limit of the climbing constraint, E represents the expected cost function, {} + Indicates that the result is positive.

4. An online electric energy dispatching and adjustment system for a power system, characterized in that: include: A constraint condition confirmation module is used to determine online demand adjustment constraint conditions; wherein the online demand adjustment constraint conditions include: demand adjustment constraint, real-time market power generation constraint and ramp power generation constraint; An online demand adjustment model establishment module, used to establish an online demand adjustment model according to the constraint conditions; An online demand adjustment model calculation module is used to obtain input values of the online demand adjustment model and input the input values into the online demand adjustment model to obtain online demand adjustment results; wherein the input values include: the user power demand adjustment size and the user power demand adjustable range; The constraint function of the demand adjustment constraint is expressed by the following formula: g 1,i,t (x i,t )= α i D i,t -x i,t ≤0; Among them, x i,t is the electricity demand, g 1,i,t (x i,t ) and g 2,i,t (x i,t ) are all constraint functions of demand adjustment constraints, α i is the demand-adjusted lower bound, is the upper bound of demand adjustment, D i,t Actual electricity demand; The online demand adjustment model is expressed by the following formula: Among them, x i,kT+t is the electricity demand at sampling time kT+t, B is the adjustable range of user electricity demand, T is the maximum sampling time, x i,t is the electricity demand, C i,t (x i,t ) is the cost function, E represents the expected cost function, {} + Indicates that the result is positive, g j,i,t (x i,t ) is the constraint function, g 1,i,t (x i,t ) and g 2,i,t (x i,t ) are both constraint functions of demand adjustment constraints, g 3,i,t (x i,t ) is the constraint function of real-time market power generation constraint, g 4,i,t (x i,t ,x i,t-1 ) and g 5,i,t (x i,t ,x i,t-1 ) are all constraint functions of ramp power generation constraints, x is the actual power demand, To predict electricity demand, R is the maximum value of the adjustable range of electricity demand; Among them, π t represents the cost of electricity generated on the day before, π s and π e They correspond to the real-time market unit shortage penalty and excess penalty, To predict electricity demand.

5. The online electric energy dispatching and adjusting system of the electric power system according to claim 4, characterized in that: The constraint function of the real-time market power generation constraint is expressed by the following formula: Among them, x i,t is the electricity demand, g 3,i,t (x i,t ) is the constraint function of the real-time market power generation constraint, To predict electricity demand, is the upper limit of power generation, E represents the expected cost function, {} + Indicates that the result is positive.

6. The online electric energy dispatching and adjusting system of the electric power system according to claim 4, characterized in that: The constraint function of the ramp power generation constraint is expressed by the following formula: Among them, x i,t and x i,t-1 is the electricity demand at two consecutive sampling times t-1 and t, g 4,i,t (x i,t ,x i,t-1 ) and g 5,i,t (x i,t ,x i,t-1 ) are all constraint functions for ramp power generation constraints, and is the predicted electricity demand at two consecutive sampling times t-1 and t, is the upper limit of the climbing constraint, r i is the lower limit of the climbing constraint, E represents the expected cost function, {} + Indicates that the result is positive.

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