New energy consumption collaborative optimization method and system based on system new energy utilization rate

Abstract

The invention discloses a new energy consumption collaborative optimization method and system based on a system new energy utilization rate. The method comprises the steps that the system new energy utilization rate is calculated according to a single equipment basic attenuation index, temperature efficiency, humidity corrosion efficiency and maintenance efficiency recovery amount; according to a real-time correlation mechanism of the carbon counteracting intensity and new energy output, new energy constraints are constructed, and the new energy constraints comprise a carbon emission intensity constraint and a multi-time scale coupling constraint; and under the carbon emission intensity constraint and the multi-time scale coupling constraint, establishing a collaborative optimization objective function according to the system new energy utilization rate, and solving the multi-objective collaborative optimization objective function to obtain a new energy consumption collaborative scheduling scheme. The new energy utilization rate of the system is effectively improved, the carbon emission intensity is reduced, internal optimization is carried out on a high-proportion new energy power system, and a scheduling strategy is determined.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimal dispatching of new energy power systems, and particularly relates to a collaborative optimization method and system for new energy consumption based on the new energy utilization rate of the system. Background Art

[0002] With the rapid increase in the new energy penetration rate, the power system faces the dual challenges of limited consumption capacity and low-carbon transformation. Traditional new energy consumption models mainly rely on static efficiency assumptions and do not fully consider the dynamic efficiency decay problems of devices such as photovoltaics and wind power during operation due to environmental aging, temperature and humidity changes, and maintenance lags, resulting in significant deviations in output prediction and severely restricting the dispatching accuracy. Existing research mostly focuses on single-objective optimization, and the proposed minimum thermal power generation cost models do not establish a multi-objective collaborative mechanism for thermal power-carbon cost-new energy consumption. In addition, current carbon constraint models mostly use fixed carbon emission coefficients and fail to optimize the allocation of generation rights through the dynamic correlation between the carbon offset intensity and new energy output. Industry practice shows that in systems with more than 30% new energy, traditional methods are prone to cause two types of problems: 1) Excessive curtailment of electricity to ensure grid stability, resulting in an annual utilization rate loss of 8%-12%; 2) The carbon cost accounting is out of touch with the real-time output, leading to low carbon asset utilization efficiency. Therefore, it is urgent to develop a collaborative optimization system that integrates the dynamic characteristics of devices and multi-dimensional carbon constraints to break through the technical bottleneck that it is difficult to balance the economy, low carbon, and stability of high-proportion new energy systems. Summary of the Invention

[0003] The present invention provides a collaborative optimization method and system for new energy consumption based on the new energy utilization rate of the system to solve the technical problem that traditional new energy consumption models do not consider dynamic efficiency decay.

[0004] In a first aspect, the present invention provides a collaborative optimization method for new energy consumption based on dynamic efficiency decay, including: Obtaining the basic decay index of a single device, temperature efficiency, humidity corrosion efficiency, and maintenance efficiency recovery amount; Calculating the new energy utilization rate of the system according to the basic decay index of the single device, the temperature efficiency, the humidity corrosion efficiency, and the maintenance efficiency recovery amount, where the expression of the new energy utilization rate of the system is: , In the formula, is the new energy utilization rate of the system within the T time period, is the total output of the system at time t, is the theoretical maximum output of the system at time t, is the total dispatching period; According to the real-time correlation mechanism between carbon offset intensity and new energy output, new energy constraints are constructed. The new energy constraints include carbon emission intensity constraints and multi-time scale coupling constraints; Under the carbon emission intensity constraints and the multi-time scale coupling constraints, a collaborative optimization objective function is established according to the new energy utilization rate of the system. The expression of the collaborative optimization objective function is: , , In the formula, is the collaborative optimization objective function, is the new energy curtailment cost, is the operating cost of thermal power units, is the carbon emission cost, is the grid fluctuation penalty cost, is the utilization rate reward coefficient, is the new energy utilization rate of the system within the T time period, is the electricity price of equipment i for curtailment at time t, is the new energy utilization rate income item, is the theoretical maximum output of new energy equipment i at time t; The collaborative optimization objective function is solved to obtain a collaborative dispatching scheme for new energy accommodation.

[0005] In a second aspect, the present invention provides a collaborative optimization system for new energy accommodation based on dynamic efficiency decay, including: An acquisition module configured to acquire the basic decay index of a single device, temperature efficiency, humidity corrosion efficiency, and maintenance efficiency recovery amount; A calculation module configured to calculate the new energy utilization rate of the system according to the basic decay index of the single device, the temperature efficiency, the humidity corrosion efficiency, and the maintenance efficiency recovery amount. Among them, the expression of the new energy utilization rate of the system is: , In the formula, is the new energy utilization rate of the system within the T time period, is the total output of the system at time t, is the theoretical maximum output of the system at time t, is the total dispatching period; A first construction module configured to construct new energy constraints according to the real-time correlation mechanism between carbon offset intensity and new energy output. The new energy constraints include carbon emission intensity constraints and multi-time scale coupling constraints; A second construction module configured to establish a collaborative optimization objective function according to the new energy utilization rate of the system under the carbon emission intensity constraints and the multi-time scale coupling constraints. The expression of the collaborative optimization objective function is: , , In the formula, is the collaborative optimization objective function, is the new energy abandonment cost, is the operating cost of thermal power units, is the carbon emission cost, is the power grid fluctuation penalty cost, is the utilization rate reward coefficient, is the system new energy utilization rate within the T time period, is the electricity price of equipment i for abandoning electricity at time t, is the new energy utilization rate benefit term, is the theoretical maximum output of new energy equipment i at time t; The solution module is configured to solve the collaborative optimization objective function to obtain a new energy consumption collaborative scheduling scheme.

[0006] In a third aspect, an electronic device is provided, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the new energy consumption collaborative optimization method based on the system new energy utilization rate according to any embodiment of the present invention.

[0007] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program instructions are executed by a processor, the processor is enabled to execute the steps of the new energy consumption collaborative optimization method based on the system new energy utilization rate according to any embodiment of the present invention.

[0008] The new energy consumption collaborative optimization method and system based on the system new energy utilization rate of the present application combine the dynamic efficiency decay characteristics of equipment with multi-dimensional carbon constraints to achieve the collaborative optimization of a high-proportion new energy power system, and specifically produce the following remarkable technical effects: 1) Significantly improve the new energy prediction accuracy and system utilization rate: By establishing a dynamic efficiency calculation model including the basic decay index of a single device, temperature efficiency, humidity corrosion efficiency, and maintenance efficiency recovery amount, and calculating the theoretical maximum output of the device and the theoretical maximum output of the system based on this, the system new energy utilization rate is finally accurately calculated.

[0009] Specifically, the model fully considers the impacts of equipment aging, environmental changes (temperature and humidity), and maintenance during operation on efficiency, effectively overcomes the output prediction deviation problem caused by traditional static efficiency models, and significantly improves the accuracy of new energy output prediction. This enables the system to more accurately evaluate the accommodation potential, provides a basis for subsequent optimized scheduling, and directly contributes to the effective improvement of the new energy utilization rate of the system.

[0010] 2) Achieve multi-objective collaborative optimization and reduce the comprehensive cost and carbon emission intensity: The objective function comprehensively considers the new energy curtailment cost, thermal power unit operation cost, carbon emission cost, and grid fluctuation penalty cost, and strictly satisfies constraint conditions such as carbon emission intensity constraints during the optimization process. This design enables the new energy accommodation collaborative scheduling scheme to minimize new energy curtailment, optimize the thermal power operation cost, minimize the carbon emission cost, and control the grid fluctuation penalty while ensuring the safety of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 It is a flowchart of a new energy accommodation collaborative optimization method based on the new energy utilization rate of the system provided by an embodiment of the present invention; Figure 2 It is a structural block diagram of a new energy accommodation collaborative optimization system based on the new energy utilization rate of the system provided by an embodiment of the present invention; Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0014] Please refer to Figure 1 , which shows a flowchart of a new energy accommodation collaborative optimization method based on the new energy utilization rate of the present application.

[0015] As shown in Figure 1As shown in the figure, the new energy consumption collaborative optimization method based on the new energy utilization rate of the system specifically includes the following steps: Step S101: Obtain the basic attenuation index, temperature efficiency, humidity corrosion efficiency, and maintenance efficiency recovery amount of a single device.

[0016] In this step, calculate the basic attenuation index of a single device. The expression is: , In the formula, is the basic attenuation index of the new energy device i at time t, is the initial efficiency of the new energy device i, is the annual attenuation coefficient of the new energy device i; is the set of new energy devices; The annual attenuation coefficient of the new energy device i is obtained by fitting long-term operation data and is the attenuation coefficient deduced from actual operation data.

[0017] Calculate the temperature efficiency. The expression is: , In the formula, is the temperature efficiency of the new energy device i at time t, is the temperature sensitivity coefficient of the new energy device i, is the ambient temperature at time t, is the reference temperature of the new energy device i; The temperature sensitivity coefficient of the new energy device i is obtained in the following way: Controlled variable experiment: Under constant light / wind speed, record the curve of device output power changing with temperature (such as the I-V curve of a photovoltaic module). Efficiency calculation: Measure the ratio of the actual output power to the theoretical maximum output power at different temperatures (i.e., efficiency ). Linear regression: Perform a linear fit on the efficiency and the temperature difference, and the slope is .

[0018] Calculate the humidity corrosion efficiency. The expression is: , In the formula, is the humidity corrosion efficiency of the new energy device i at time t, is the humidity sensitivity coefficient of the new energy device i, is the relative humidity at time t, is the reference humidity of the new energy device i; Calculate the maintenance efficiency recovery amount. The expression is: , In the formula, is the efficiency recovery amount of the new energy device i after the k-th maintenance, is the maximum recoverable efficiency per single maintenance, is the maintenance effect attenuation time constant of the new energy device i, is the time point of the k-th maintenance.

[0019] It can be calculated through the fouling shielding rate (such as the loss of light transmittance caused by dust) and the light transmittance recovery ratio after maintenance. For example:

[0020] Among them, and are the efficiencies in the clean and polluted states respectively.

[0021] Step S102, calculate the new energy utilization rate of the system according to the single-device basic attenuation index, the temperature efficiency, the humidity corrosion efficiency, and the maintenance efficiency recovery amount.

[0022] In this step, calculate the comprehensive dynamic efficiency of a single device according to the single-device basic attenuation index, temperature efficiency, humidity corrosion efficiency, and maintenance efficiency recovery amount. The expression is: , In the formula, is the comprehensive dynamic efficiency of the new energy device i at time t, is the cumulative maintenance times of the new energy device i; Calculate the theoretical maximum output according to the comprehensive dynamic efficiency of a single device. The expression is: , In the formula, is the effective area or swept area of the new energy device i, is the resource intensity of the new energy device i at time t; It is affected by geographical location, weather, and time: , In the formula, is the direct radiation (perpendicular to the sun's rays) at time t; is the angle between the inclination angle of the photovoltaic panel and the solar incidence angle; is the diffuse radiation (on cloudy days or atmospheric reflection).

[0023] Calculate the actual output according to the theoretical maximum output. The expression is: , In the formula, is the actual output of the new energy device i at time t, is the curtailment rate of new energy equipment i at time t; Calculate the total system output based on the actual output of a single device. The expression is: , In the formula, is the set of new energy equipment; Calculate the upper limit of the theoretical system output. The expression is: , Calculate the new energy utilization rate of the system based on the upper limit of the theoretical system output and the total system output. The expression for the new energy utilization rate of the system is: , In the formula, is the new energy utilization rate of the system within the T time period, is the total system output at time t, is the maximum theoretical output of the system at time t, is the total scheduling period.

[0024] Step S103: Construct new energy constraints according to the real-time correlation mechanism between carbon offset intensity and new energy output. The new energy constraints include carbon emission intensity constraints and multi-time scale coupling constraints.

[0025] In this step, the expression for the carbon emission intensity constraint is: , In the formula, is the carbon emission coefficient of thermal power unit j, is the output of thermal power unit j at time t, is the total electricity power demand that the system needs to meet at time t, is the maximum allowable system carbon intensity. T is the set of time periods, and J is the set of thermal power units.

[0026] The expression for the multi-time scale coupling constraint is: , In the formula, is the time window, is the new energy utilization rate of the system at time is the average utilization rate threshold within the window.

[0027] Furthermore, the new energy constraints also include: Power balance constraint. The expression is: , In the formula, is the output of thermal power unit j at time t, is the load demand at time t, is the energy storage charging power at time t, is the network loss at time t, is the energy storage discharging power at time t, is the total system output at time t; The upper and lower limits of thermal power output, the expression is: , In the formula, is the minimum output of thermal power unit j, is the maximum output of thermal power unit j; The energy storage boundary, the expression is: , In the formula, is the minimum value of the energy storage, is the energy storage amount at time t, is the maximum value of the energy storage; The new energy maximum power limit, the expression is: , In the formula, is the maximum curtailment rate of new energy device i at time t, is the power rate of new energy i at time t; The frequency deviation constraint, the expression is: , In the formula, is the frequency deviation of the system at time t, is the maximum frequency; The spinning reserve constraint, the expression is: , In the formula, is the maximum installed capacity of unit j, is the set of unit j, is the upward regulation reserve demand at time t; The new energy penetration rate constraint, the expression is: , In the formula, is the total system output at time t, is the system load output at time t, is the minimum new energy penetration rate; The lower limit of equipment efficiency decay, the expression is: , In the formula, is the actual efficiency of equipment i at time t, is the lower limit of efficiency decay of equipment i.

[0028] Step S104, under the carbon emission intensity constraint and the multi-time scale coupling constraint, establish a collaborative optimization objective function according to the new energy utilization rate of the system.

[0029] In this step, the expression for calculating the new energy curtailment cost is: , , where, is the new energy curtailment cost, is the unit curtailment penalty cost of the new energy equipment , is the power generation rate of new energy i at time t, is the curtailment cost coefficient, is the set of new energy equipment; The power generation rate of new energy i at time t, that is, the proportion of unabsorbed output, is determined by the dispatching strategy or system constraints.

[0030]

[0031] represents the actual output (the actually absorbed power) of equipment i at time t, represents the theoretical maximum output (the maximum power generation capacity under ideal conditions) of equipment i at time t; The expression for calculating the operating cost of thermal power units is: , where, is the operating cost of thermal power units, is the set of thermal power units, , and are the coefficients of the quadratic term, the linear term and the constant of the quadratic function respectively, is the output of thermal power unit j at time t; The expression for calculating the carbon emission cost is: , , where, is the carbon emission cost, is the carbon price, is the carbon emission coefficient of thermal power unit j, is the carbon offset intensity of new energy equipment i, is the actual output of new energy equipment i at time t, is the set of thermal power units, is the set of new energy equipment, is the set of time periods; The expression for calculating the grid fluctuation penalty cost is as follows: , wherein, is the grid fluctuation penalty cost, is the fluctuation penalty coefficient, is the grid power fluctuation amount at time t.

[0032] Furthermore, the expression for the collaborative optimization objective function is as follows: , , wherein, is the collaborative optimization objective function, is the new energy curtailment cost, is the operating cost of thermal power units, is the carbon emission cost, is the grid fluctuation penalty cost, is the utilization rate reward coefficient, is the system new energy utilization rate within the T period, is the electricity price of equipment i for curtailment at time t, is the new energy utilization rate revenue item, is the theoretical maximum output of new energy equipment i at time t.

[0033] Step S105: Solve the collaborative optimization objective function to obtain a collaborative scheduling scheme for new energy accommodation.

[0034] In this step, piecewise linearization (PWL) and second-order cone programming (SOCP) are used to transform the non-linear terms. For example, the non-linear terms can be transformed when calculating the operating cost of thermal power units and the grid fluctuation penalty cost.

[0035] The Benders decomposition algorithm is used to handle large-scale coupling constraints, or rolling horizon optimization (RHO) is adopted for online solution. For example, when dealing with integer variables (such as equipment maintenance status) and coupling constraints (such as carbon emission intensity constraints).

[0036] Call commercial solvers such as CPLEX / Gurobi to calculate the transformed optimization model; Finally, output a scheduling scheme including the new energy curtailment rate, thermal power output, and energy storage strategy.

[0037] In summary, the method of the present application fully considers the impacts of equipment aging, environmental changes (temperature and humidity), and maintenance on efficiency during the operation process, effectively overcomes the output prediction deviation problem caused by the traditional static efficiency model, and significantly improves the accuracy of new energy output prediction. This enables the system to more accurately evaluate the accommodation potential, provides a basis for subsequent optimized scheduling, directly contributes to the effective improvement of the new energy utilization rate of the system, and the objective function comprehensively considers the new energy abandonment cost, thermal power unit operation cost, carbon emission cost, and grid fluctuation penalty cost, and strictly meets the constraint conditions such as the carbon emission intensity constraint during the optimization process. This design enables the new energy accommodation collaborative scheduling scheme to minimize new energy abandonment, optimize the thermal power operation cost, minimize the carbon emission cost, and control the grid fluctuation penalty while ensuring the safety of the power grid.

[0038] Please refer to Figure 2 , which shows a structural block diagram of a new energy accommodation collaborative optimization system based on the new energy utilization rate of the system of the present application.

[0039] As Figure 2 shown, the new energy accommodation collaborative optimization system 200 includes an acquisition module 210, a calculation module 220, a first construction module 230, a second construction module 240, and a solution module 250.

[0040] Among them, the acquisition module 210 is configured to acquire the single-device basic attenuation index, temperature efficiency, humidity corrosion efficiency, and maintenance efficiency recovery amount; The calculation module 220 is configured to calculate the new energy utilization rate of the system according to the single-device basic attenuation index, the temperature efficiency, the humidity corrosion efficiency, and the maintenance efficiency recovery amount, where the expression of the new energy utilization rate of the system is: , In the formula, is the new energy utilization rate of the system within the T time period, is the total system output at time t, is the theoretical maximum output of the system at time t, is the total scheduling period; The first construction module 230 is configured to construct new energy constraints according to the real-time association mechanism between the carbon offset intensity and the new energy output, and the new energy constraints include a carbon emission intensity constraint and a multi-time scale coupling constraint; The second construction module 240 is configured to establish a collaborative optimization objective function according to the new energy utilization rate of the system under the carbon emission intensity constraint and the multi-time scale coupling constraint, and the expression of the collaborative optimization objective function is: , , In the formula, is the collaborative optimization objective function, is the cost of abandoned electricity of new energy, is the operating cost of thermal power units, is the carbon emission cost, is the grid fluctuation penalty cost, is the utilization rate reward coefficient, is the system new energy utilization rate within the T period, is the electricity price of equipment i abandoning electricity at time t, is the new energy utilization rate benefit item, is the theoretical maximum output of new energy equipment i at time t; The solution module 250 is configured to solve the collaborative optimization objective function to obtain a new energy consumption collaborative scheduling scheme.

[0041] It should be understood that Figure 2 The various modules described in Figure 1 correspond to the respective steps in the method described in the reference Figure 2 Therefore, the operations, features, and corresponding technical effects described above for the method also apply to the various modules in

[0042] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored. When the program instructions are executed by a processor, the processor is caused to execute the new energy consumption collaborative optimization method based on the system new energy utilization rate in any of the above method embodiments; As an implementation manner, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set as: Obtain the single-device basic attenuation index, temperature efficiency, humidity corrosion efficiency, and maintenance efficiency recovery amount; Calculate the system new energy utilization rate according to the single-device basic attenuation index, the temperature efficiency, the humidity corrosion efficiency, and the maintenance efficiency recovery amount, where the expression of the system new energy utilization rate is: , In the formula, is the system new energy utilization rate within the T period, is the total system output at time t, is the theoretical maximum output of the system at time t, is the total scheduling period; Construct new energy constraints according to the real-time association mechanism between carbon offset intensity and new energy output, and the new energy constraints include carbon emission intensity constraints and multi-time scale coupling constraints; Under the carbon emission intensity constraint and the multi-time scale coupling constraint, a collaborative optimization objective function is established according to the new energy utilization rate of the system. The expression of the collaborative optimization objective function is as follows: , , In the formula, is the collaborative optimization objective function, is the new energy curtailment cost, is the operating cost of thermal power units, is the carbon emission cost, is the grid fluctuation penalty cost, is the utilization rate reward coefficient, is the new energy utilization rate of the system in period T, is the electricity price of equipment i for curtailment at time t, is the new energy utilization rate revenue term, is the theoretical maximum output of new energy equipment i at time t; Solve the collaborative optimization objective function to obtain a new energy accommodation collaborative scheduling scheme.

[0043] A computer-readable storage medium may include a storage program area and a storage data area. Among them, the storage program area can store an operating system and application programs required for at least one function; the storage data area can store data created according to the use of the new energy accommodation collaborative optimization system based on the system new energy utilization rate, etc. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the computer-readable storage medium may optionally include a memory remotely provided relative to the processor, and these remote memories can be connected to the new energy accommodation collaborative optimization system based on the system new energy utilization rate through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0044] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 3 shown, the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected through a bus or other means, Figure 3Take the bus connection as an example. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 320, that is, implements the new energy consumption collaborative optimization method based on the new energy utilization rate of the system in the above method embodiment. The input device 330 can receive input digital or character information, and generate key signal inputs related to user settings and function controls of the new energy consumption collaborative optimization system based on the new energy utilization rate of the system. The output device 340 may include display devices such as a display screen.

[0045] The above electronic device can execute the method provided by the embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in this embodiment, reference can be made to the method provided by the embodiment of the present invention.

[0046] As an implementation manner, the above electronic device is applied to the new energy consumption collaborative optimization system based on the new energy utilization rate of the system, and is used for the client, including: at least one processor; and a memory communicatively connected to at least one processor; wherein, the memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to: Obtain the single-device basic attenuation index, temperature efficiency, humidity corrosion efficiency, and maintenance efficiency recovery amount; Calculate the new energy utilization rate of the system according to the single-device basic attenuation index, the temperature efficiency, the humidity corrosion efficiency, and the maintenance efficiency recovery amount, wherein the expression of the new energy utilization rate of the system is: , In the formula, is the new energy utilization rate of the system within the T time period, is the total system output at time t, is the theoretical maximum output of the system at time t, is the total scheduling period; According to the real-time correlation mechanism between carbon offset intensity and new energy output, construct new energy constraints, and the new energy constraints include carbon emission intensity constraints and multi-time scale coupling constraints; Under the carbon emission intensity constraints and the multi-time scale coupling constraints, establish a collaborative optimization objective function according to the new energy utilization rate of the system, and the expression of the collaborative optimization objective function is: , , In the formula, is the collaborative optimization objective function, is the new energy curtailment cost, is the operating cost of the thermal power unit, is the carbon emission cost, is the grid fluctuation penalty cost, is the utilization rate reward coefficient, is the system new energy utilization rate during period T, is the electricity price of the abandoned electricity of equipment i at time t, is the new energy utilization rate revenue item, is the theoretical maximum output of the new energy equipment i at time t; Solve the collaborative optimization objective function to obtain a collaborative dispatching scheme for new energy consumption.

[0047] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solutions, in essence, or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A new energy consumption collaborative optimization method based on the new energy utilization rate of the system, characterized in that, Including: Obtain the basic attenuation index of a single device, temperature efficiency, humidity corrosion efficiency, and the recovery amount of maintenance efficiency; Calculate the new energy utilization rate of the system according to the basic attenuation index of the single device, the temperature efficiency, the humidity corrosion efficiency, and the recovery amount of maintenance efficiency, where the expression of the new energy utilization rate of the system is: ， Wherein, is the system's new energy utilization rate during the T period, is the total system output at time t, is the theoretical maximum output of the system at time t, is the total dispatching period; Construct new energy constraints according to the real-time correlation mechanism between carbon offset intensity and new energy output, where the new energy constraints include carbon emission intensity constraints and multi-time scale coupling constraints; Under the carbon emission intensity constraints and the multi-time scale coupling constraints, establish a collaborative optimization objective function according to the new energy utilization rate of the system, where the expression of the collaborative optimization objective function is: ， ， In the formula, is the collaborative optimization objective function, is the cost of abandoned new energy, is the operating cost of thermal power units, is the carbon emission cost, is the penalty cost for grid fluctuations, is the utilization rate reward coefficient, is the system new energy utilization rate within period T, is the electricity price of abandoned power of equipment i at moment t, is the new energy utilization rate revenue term, is the theoretical maximum output of new energy equipment i at moment t; Solve the collaborative optimization objective function to obtain a collaborative scheduling plan for new energy consumption.

2. The new energy consumption collaborative optimization method based on the new energy utilization rate of the system according to claim 1, wherein, The obtaining of the basic attenuation index of a single device, temperature efficiency, humidity corrosion efficiency, and the recovery amount of maintenance efficiency includes: Calculate the basic attenuation index of a single device, and the expression is: ， In the formula, is the basic decay index of the new energy device i at time t, is the initial efficiency of the new energy device i, is the annual decay coefficient of the new energy device i; is the set of new energy devices; Calculate the temperature efficiency, and the expression is: ， In the formula, is the temperature efficiency of the new energy device i at time t, is the temperature sensitivity coefficient of the new energy device i, is the ambient temperature at time t, is the reference temperature of the new energy device i; Calculate the humidity corrosion efficiency, and the expression is: ， Wherein, is the humidity corrosion efficiency of the new energy device i at time t, is the humidity sensitivity coefficient of the new energy device i, is the relative humidity at time t, is the reference humidity of the new energy device i; Calculate the recovery amount of maintenance efficiency, and the expression is: ， Wherein, is the efficiency recovery amount after the k-th maintenance of the new energy device i, is the maximum recoverable efficiency per single maintenance, is the maintenance effect attenuation time constant of the new energy device i, is the time point of the k-th maintenance.

3. The new energy consumption collaborative optimization method based on the new energy utilization rate of the system according to claim 2, wherein The calculating of the new energy utilization rate of the system according to the basic attenuation index of the single device, the temperature efficiency, the humidity corrosion efficiency, and the recovery amount of maintenance efficiency includes: Calculate the comprehensive dynamic efficiency of a single device according to the basic attenuation index of the single device, the temperature efficiency, the humidity corrosion efficiency, and the recovery amount of maintenance efficiency, and the expression is: ， Wherein, is the comprehensive dynamic efficiency of the new energy device i at time t; is the cumulative maintenance times of the new energy device i; Calculate the theoretical maximum output according to the comprehensive dynamic efficiency of the single device, and the expression is: ， In the formula, is the effective area or swept area of the new energy device i, is the resource intensity of the new energy device i at time t; Calculate the actual output according to the theoretical maximum output, and the expression is: ， In the formula, is the actual output of the new energy device i at time t, is the curtailment rate of the new energy device i at time t; Calculate the total output of the system according to the actual output of a single device, and the expression is: ， In the formula, is the set of new energy devices; Calculate the upper limit of the theoretical output of the system, and the expression is: ， Calculate the new energy utilization rate of the system according to the upper limit of the theoretical output of the system and the total output of the system.

4. A new energy consumption collaborative optimization method based on the new energy utilization rate of the system, characterized in that Wherein, The expression of the carbon emission intensity constraint is: ， In the formula, is the carbon emission coefficient of thermal power unit j, is the output of thermal power unit j at time t, is the total electricity power demand that the system needs to meet at time t, is the maximum allowable system carbon intensity, T is the set of time periods, and J is the set of thermal power units.

5. A new energy consumption collaborative optimization method based on the new energy utilization rate of the system, characterized in that, Wherein, The expression of the multi-time scale coupling constraint is: ， In the formula, is the time window, is the system's new energy utilization rate at time is the average utilization rate threshold within the window.

6. A new energy consumption collaborative optimization method based on the new energy utilization rate of the system, characterized in that, The expression for calculating the new energy abandonment cost is: ， ， Wherein, is the new energy curtailment cost, is the new energy equipment unit curtailment penalty cost, is the power rate of new energy i at time t, is the curtailment cost coefficient, is the set of new energy equipment; The expression for calculating the operating cost of the thermal power unit is: ， In the formula, is the operating cost of the thermal power unit, is the set of thermal power units, , and are the coefficients of the quadratic term and the linear term of the quadratic function and the constant respectively, is the output of thermal power unit j at time t; The expression for calculating the carbon emission cost is: ， Wherein, is the carbon emission cost, is the carbon price, is the carbon emission coefficient of thermal power unit j, is the carbon offset intensity of new energy device i, is the actual output of new energy device i at time t, is the set of thermal power units, is the set of new energy devices, is the set of time periods; The expression for calculating the grid fluctuation penalty cost is: ， Wherein, is the grid fluctuation penalty cost, is the fluctuation penalty coefficient, is the grid power fluctuation amount at time t.

7. A new energy consumption collaborative optimization method based on the new energy utilization rate of the system, characterized in that The new energy constraints further include: Power balance constraint, and the expression is: ， wherein, is the output of thermal power unit j at time t, is the load demand at time t, is the energy storage charging power at time t, is the network loss at time t, is the energy storage discharging power at time t, is the total system output at time t; Upper and lower limits of thermal power output, and the expression is: ， In the formula, is the minimum output of thermal power unit j, is the maximum output of thermal power unit j; Energy storage boundary, and the expression is: ， In the formula, is the minimum energy storage, is the energy storage at time t, is the maximum energy storage; New energy maximum power rate limit, and the expression is: ， In the formula, is the maximum curtailment rate of new energy device i at time t, is the power rate of new energy i at time t; Frequency deviation constraint, and the expression is: ， In the formula, is the frequency deviation of the system at time t, is the maximum frequency; Spinning reserve constraint, and the expression is: ， wherein, is the maximum installed capacity of unit j, is the set of unit j, is the upward reserve requirement at time t New energy penetration rate constraint, and the expression is: ， In the formula, is the total system output at time t, is the system load output at time t, is the minimum penetration rate of new energy; Lower limit of equipment efficiency attenuation, and the expression is: ， Wherein, is the actual efficiency of device i at time t, is the lower limit of the efficiency decay of device i.

8. A new energy consumption collaborative optimization system based on the new energy utilization rate of the system, characterized in that, Including: An obtaining module configured to obtain the basic attenuation index of a single device, temperature efficiency, humidity corrosion efficiency, and the recovery amount of maintenance efficiency; A calculating module configured to calculate the new energy utilization rate of the system according to the basic attenuation index of the single device, the temperature efficiency, the humidity corrosion efficiency, and the recovery amount of maintenance efficiency, where the expression of the new energy utilization rate of the system is: ， Wherein, is the new energy utilization rate of the system within the T period, is the total output of the system at time t, is the theoretical maximum output of the system at time t, is the total dispatching period; A first construction module configured to construct new energy constraints according to the real-time correlation mechanism between carbon offset intensity and new energy output, where the new energy constraints include carbon emission intensity constraints and multi-time scale coupling constraints; The second construction module is configured to establish a collaborative optimization objective function according to the new energy utilization rate of the system under the carbon emission intensity constraint and the multi-time scale coupling constraint, and the expression of the collaborative optimization objective function is: ， ， In the formula, is the collaborative optimization objective function, is the cost of curtailed new energy, is the operating cost of thermal power units, is the carbon emission cost, is the penalty cost for grid fluctuations, is the utilization rate reward coefficient, is the system new energy utilization rate within period T, is the electricity price of curtailed power of device i at time t, is the new energy utilization rate revenue term, is the theoretical maximum output of new energy device i at time t; The solving module is configured to solve the collaborative optimization objective function to obtain a collaborative scheduling scheme for new energy consumption.

9. An electronic device, characterized in that, It includes: At least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 7.

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