A method, system, equipment and medium for the joint operation control of new energy and energy storage

Abstract

This invention relates to the field of joint operation technology of new energy and energy storage, specifically to a control method, system, equipment, and medium for joint operation of new energy and energy storage. The steps are as follows: Based on the characteristics of wind and solar power output, a joint operation mode for new energy and energy storage is determined. This joint operation mode includes local / wide-area joint operation modes. Based on the mode call, a multi-objective collaborative optimization scheduling model is established. Constraints are set, and a genetic algorithm is used to solve the multi-objective collaborative optimization scheduling model to obtain a Pareto optimal solution set. Based on the Pareto optimal solution set, the optimal scheduling operation scheme is determined. This invention guides energy storage resources to operate in a way that best improves the overall system operating efficiency by dynamically selecting the optimal joint operation mode. This changes the problems of low utilization and poor economic efficiency of traditional configuration modes, significantly improving the value contribution of energy storage assets in supporting the realization of the dual-carbon goals of the new power system.

Description

Technical Field

[0001] This invention relates to the field of new energy and energy storage joint operation technology, and more specifically, to a control method, system, equipment and medium for the joint operation of new energy and energy storage. Background Technology

[0002] Currently, research on the coordinated scheduling of new energy and energy storage for low-carbon goals mainly focuses on optimizing single or pre-fixed operation modes.

[0003] On the one hand, a large amount of research focuses on localized "site-level" coordination, that is, configuring dedicated energy storage within a single renewable energy site or cluster. The optimization objective is mostly focused on directly smoothing the output fluctuations of the site to improve local grid-friendliness and reduce energy curtailment. Although such solutions can improve the power quality of local access points to some extent, their optimization perspective is limited to the site itself, making it difficult to effectively respond to the global system balance needs and low-carbon goals. Moreover, the over-utilization of energy storage capacity due to local smoothing needs may limit its contribution efficiency to system-level low-carbon goals.

[0004] On the other hand, another mainstream research focuses on the coordinated scheduling of system-level energy storage and the overall output of new energy sources. This approach treats all new energy sources in the system as a whole, with energy storage primarily responsible for smoothing fluctuations in the system's net load. Theoretically, this type of approach is more conducive to optimizing the overall economic operation of the system and reducing total carbon emissions at the system level. However, existing solutions typically employ a single, fixed collaborative architecture, forcibly implementing the same mode regardless of changes in wind and solar resource characteristics and output dynamics. This lack of ability to dynamically select the optimal collaborative mechanism based on real-time or predicted scenario characteristics is a limitation. It ignores the critical impact that different new energy resource endowments and output characteristics may have on the selection of the optimal collaborative architecture, and it cannot flexibly respond to diverse actual operating scenarios to maximize the system's low-carbon benefits.

[0005] Although some studies have begun to recognize the shortcomings of a single fixed coordination mode and have explored comparisons of different configuration strategies (centralized / decentralized) in wind-solar-storage coordination, current technical solutions that can effectively support dynamic mode decision-making are still insufficient. Existing research involving multi-mode evaluation is mostly offline, scenario-based comparative analysis. That is, for several preset typical scenarios, the optimized scheduling results under different fixed modes are calculated and the performance is compared. In the end, it often gives a static, either-or conclusion. Such static conclusions are difficult to apply to the dynamically changing actual power grid operating environment.

[0006] Furthermore, while multi-objective optimization has been widely adopted in terms of optimization models, there are few studies that regard the dynamic selection of the model itself as part of the optimization process and establish a rule-based decision-making mechanism that strongly correlates it with scenario characteristics. In particular, there are few studies that combine scenario classification rules that finely characterize power output features, such as wind power-dominated, photovoltaic-dominated, and simultaneous / asynchronous changes in wind and solar power.

[0007] In summary, existing solutions fail to model mode selection itself as an automatically executed decision-making process driven by real-time scenario characteristics, and also lack a systematic approach to deeply analyze the differences in the impact of various modes on low-carbon goals under different scenario characteristics and propose clear decision-making rules. Summary of the Invention

[0008] The purpose of this invention is to provide a method, system, device and medium for the joint operation control of new energy and energy storage, in order to solve the technical problems pointed out in the background art that the existing solutions fail to model the mode selection itself as a decision-making process driven by real-time scenario characteristics and can be automatically executed, and also lack a systematic method for deeply analyzing the differences in the impact of each mode on the low-carbon target under different scenario characteristics and proposing clear decision rules.

[0009] This invention is achieved through the following technical solution: a method for joint operation and control of new energy and energy storage, comprising the following steps:

[0010] The characteristics of wind and solar power output are obtained, and the joint operation mode of new energy and energy storage is determined based on the characteristics of wind and solar power output. The joint operation mode includes local joint operation mode and wide-area joint operation mode.

[0011] Based on the determined joint operation mode, the corresponding multi-objective collaborative optimization scheduling model is invoked. The multi-objective collaborative optimization scheduling model includes sub-objectives of minimizing the output of thermal power units and minimizing the maximum carbon emissions of thermal power units. The multi-objective collaborative optimization scheduling model corresponding to the local joint operation mode also includes the sub-objective of minimizing the fluctuation of the total output of new energy and energy storage. The multi-objective collaborative optimization scheduling model corresponding to the wide-area joint operation mode also includes the sub-objective of minimizing the fluctuation of the system's remaining load.

[0012] By setting constraints and using a genetic algorithm to solve the multi-objective cooperative optimization scheduling model, a Pareto optimal solution set is obtained, and the optimal scheduling operation scheme is determined based on the Pareto optimal solution set.

[0013] According to a preferred embodiment, determining the joint operation mode includes the following steps:

[0014] If the wind and solar power output characteristics are obtained, and the wind and solar power output characteristics are wind power output dominant, synchronous changes in new energy output, or only a sharp decrease in wind power output, then the local joint operation mode is selected. If the wind and solar power output characteristics are photovoltaic output dominant, asynchronous changes in wind and solar power with a decrease in wind power output, simultaneous decrease in wind and solar power output, or only a sharp decrease / sudden increase in photovoltaic power output, then the wide-area joint operation mode is selected.

[0015] According to a preferred embodiment, the multi-objective cooperative optimization scheduling model expression corresponding to the local joint operation mode is as follows:

[0016]

[0017] In the above formula, This represents the objective function of the multi-objective collaborative optimization scheduling model. This represents the sub-objective function for the output of the corresponding thermal power unit. This represents the sub-objective function for the maximum carbon emissions of the corresponding thermal power unit. This represents the sub-objective function corresponding to the fluctuation of the total output of new energy sources and energy storage;

[0018] The sub-objective function expression for the output of the corresponding thermal power unit is as follows:

[0019]

[0020] In the above formula, Indicates thermal power unit During the period of efforts, Indicates the total number of thermal power units in the system. This indicates the total number of system optimization scheduling periods. Indicates the number of hours in a time period;

[0021] The sub-objective function expression for the maximum carbon emissions of the corresponding thermal power unit is as follows:

[0022]

[0023] In the above formula, , and The carbon emission intensity coefficient for thermal power units;

[0024] The sub-objective function expression corresponding to the fluctuation of total output of new energy and energy storage is as follows:

[0025]

[0026]

[0027] In the above formula, Indicates the system's new energy and energy storage during the time period Total output This represents the average total output of the system's new energy sources and energy storage. Indicates wind farm station During the period of efforts, Indicates the number of wind farms in the system. Indicates photovoltaic power station During the period of efforts, Indicates the number of photovoltaic power stations. It is a symbolic function with a value of When the value is Time indicates energy storage During the period Discharge, when the value is Time indicates energy storage During the period Charge, Indicates energy storage During the period of efforts, This indicates the total amount of energy stored.

[0028] According to a preferred embodiment, the multi-objective cooperative optimization scheduling model expression corresponding to the wide-area joint operation mode is as follows:

[0029]

[0030] In the above formula, This represents the sub-objective function corresponding to the fluctuation of the system's remaining load;

[0031] The sub-objective function expression corresponding to the fluctuation of the system's remaining load is as follows:

[0032]

[0033]

[0034] In the above formula, Indicates the system during the time period The remaining load, This represents the average remaining load of the system. Indicates the system during the time period Total load.

[0035] According to a preferred embodiment, the constraints include:

[0036] The operating constraints of new energy sources are expressed as follows:

[0037]

[0038]

[0039] In the above formula, Indicates wind farm station The upper limit of output, Indicates photovoltaic power station The upper limit of output;

[0040] Energy storage operation constraints, expressed as:

[0041]

[0042]

[0043]

[0044]

[0045] In the above formula, Indicates energy storage The upper limit of output, Indicates energy storage The lower limit of output, Indicates energy storage During the period The amount of stored energy, Indicates energy storage The lower limit of the storage capacity, Indicates energy storage Maximum storage capacity Indicates energy storage The energy conversion efficiency;

[0046] The system operation constraints are expressed as follows:

[0047]

[0048]

[0049] In the above formula, Indicates the sending line or cross-section The lower limit of power, Indicates the sending line or cross-section Downwind farm station During the period of efforts, Indicates the sending line or cross-section Photovoltaic power station During the period of efforts, Indicates the sending line or cross-section energy storage During the period of efforts, Indicates the sending line or cross-section The upper limit of power;

[0050] The operating constraints for thermal power plants are expressed as follows:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] In the above formula, Indicates thermal power unit The lower limit of output, Indicates thermal power unit The upper limit of output, Indicates thermal power unit During the period Number of consecutive periods that have been running. It is a symbolic function with a value of or When the value is Time indicates thermal power unit During the period When it is in running state, when the value is Time indicates thermal power unit During the period It is currently in a shutdown state. Indicates thermal power unit Minimum number of runtime segments, Indicates thermal power unit During the period The number of consecutive periods of downtime. Indicates thermal power unit Minimum number of downtime periods, Indicates thermal power unit The rate of ascent, Indicates thermal power unit downhill / uphill speed.

[0057] According to a preferred embodiment, the multi-objective cooperative optimization scheduling model is solved using the NSGA-II algorithm, including the following steps:

[0058] Step S1: Generate an initial population in the solution space of the output composition of each type of unit. ;

[0059] Step S2, As a fitness function, for each individual in the population Calculate the corresponding objective function value to evaluate individual fitness;

[0060] Step S3: Calculate the fitness of each individual. The number of dominant individuals and the set of individuals dominated by it are used to rank individuals according to the number of dominant individuals, assigning each individual to the corresponding non-dominated level, and calculating the value of each individual. The degree of crowding in its non-dominated hierarchy;

[0061] Step S4: Optimize the population and generate the parent population through selection, crossover, and mutation operations;

[0062] Step S5: Merge the parent population and the offspring population to obtain a joint population. Perform non-dominated sorting on the joint population, and select individuals according to the non-dominated level and crowding to form a new generation population.

[0063] Step S6: Determine if the maximum number of iterations has been reached. If not, return to step S2. If yes, output the location of the latest generation population in the solution space to obtain the Pareto solution set.

[0064] According to a preferred embodiment, determining the optimal scheduling and operation scheme includes the following steps:

[0065] The optimal solutions in the Pareto solution set are evaluated using the ideal optimal point, as shown in the following expression:

[0066]

[0067] In the above formula, Indicates Pareto solution Euclidean distance from the ideal optimal point Indicates Pareto solution Single objective function value, This represents the ideal optimal point, which is the single-objective optimization result for each sub-objective.

[0068] The solution with the minimum Euclidean distance is the optimal scheduling and operation scheme.

[0069] This invention also provides a new energy and energy storage joint operation control system, applied to the new energy and energy storage joint operation control method described above, the system comprising:

[0070] The mode determination module is used to acquire wind and solar power output characteristics and determine the joint operation mode of new energy and energy storage based on the wind and solar power output characteristics. The joint operation mode includes a local joint operation mode and a wide-area joint operation mode.

[0071] The model determination module is used to call the corresponding multi-objective collaborative optimization scheduling model based on the determined joint operation mode. The multi-objective collaborative optimization scheduling model includes sub-objectives of minimizing the output of thermal power units and minimizing the maximum carbon emissions of thermal power units. The multi-objective collaborative optimization scheduling model corresponding to the local joint operation mode also includes the sub-objective of minimizing the fluctuation of the total output of new energy and energy storage. The multi-objective collaborative optimization scheduling model corresponding to the wide-area joint operation mode also includes the sub-objective of minimizing the fluctuation of the system's remaining load.

[0072] The solution module is used to solve the multi-objective cooperative optimization scheduling model using the NSGA-II algorithm to obtain the Pareto optimal solution set, and to determine the optimal scheduling operation scheme based on the Pareto optimal solution set.

[0073] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the new energy and energy storage joint operation control method as described above.

[0074] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the new energy and energy storage joint operation control method as described above.

[0075] The technical solution of the new energy and energy storage joint operation control method, system, equipment and medium provided by the present invention has at least the following advantages and beneficial effects: (1) By establishing a dynamic selection mechanism for wide-area / local joint operation mode based on wind and solar power output characteristics, the limitations of the existing fixed single mode scheduling scheme are overcome. In wind power-dominated or wind and solar synchronous change scenarios, the local mode is selected, and in photovoltaic-dominated or wind and solar asynchronous / sudden change scenarios, the wide-area mode is selected, which can more accurately adapt to the actual operation needs of the power system; (2) The multi-objective collaborative optimization scheduling model innovatively introduces the maximum carbon emission of thermal power units. Small sub-targets effectively suppress the marginal carbon emission deterioration problem caused by deep peak shaving; the model coordinates and optimizes the system's low-carbon target and the core operation target of new energy and energy storage under a unified framework, and achieves effective trade-off and balance among the three through the NSGA-II algorithm, which can accurately find the system's overall optimal low-carbon operation point; (3) Dynamically select the optimal joint operation mode to guide energy storage resources to operate in the way that can best improve the overall operation efficiency of the system, which changes the problem of low utilization and poor economy of the traditional configuration mode, and significantly improves the value contribution of energy storage assets in supporting the realization of the dual carbon target of the new power system. Attached Figure Description

[0076] Figure 1 This is a flowchart illustrating the combined operation control method for new energy and energy storage provided in Embodiment 1 of the present invention.

[0077] Figure 2 This is a schematic diagram of two combined operation modes of new energy and energy storage provided in Embodiment 1 of the present invention;

[0078] Figure 3 This is a schematic diagram of the model solution process provided in Embodiment 1 of the present invention;

[0079] Figure 4 This is a structural block diagram of the new energy and energy storage joint operation control system provided in Embodiment 2 of the present invention. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0081] Example 1

[0082] This invention provides a method for joint operation control of new energy and energy storage. Figure 1 This is a flowchart illustrating the control method for the combined operation of new energy and energy storage. (See attached diagram) Figure 1 As shown, the method for joint operation and control of new energy and energy storage includes the following steps:

[0083] Dynamic mode selection steps:

[0084] In this embodiment, the dynamic selection of the mode is based on the wind and solar power output characteristics, which are obtained based on the predicted or measured meteorological conditions for the future scheduling period, as shown in Table 1 below:

[0085] Table 1. Different New Energy Power Generation Scenarios and Their Characteristics

[0086]

[0087] The typical scenario is the wind and solar power output process under normal sunny and cloudy weather. When encountering other weather conditions, the wind and solar power output changes to different degrees and in different ways compared to the typical scenario, forming a short special scenario. It should be noted that this embodiment dynamically selects the optimal operating mode based on the above wind and solar power output characteristics (scenarios), which can effectively overcome the limitations of the existing fixed single mode scheduling scheme.

[0088] Specifically, in some implementations of this embodiment, the joint operation mode includes a local joint operation mode and a wide-area joint operation mode, see [link to relevant documentation]. Figure 2As shown, these two joint operation modes differ significantly in terms of the spatial relationship between new energy and energy storage, and the objects of energy storage output regulation. In the local joint operation mode, new energy and energy storage are spatially interdependent, mainly consisting of self-built distribution and storage at new energy power plants or shared distribution and storage by new energy clusters. The operation method is to use energy storage to smooth out output fluctuations at power plants or clusters, charging and storing energy when new energy output is high, and discharging to supplement when new energy output is insufficient. In the wide-area joint operation mode, new energy and energy storage are spatially relatively independent, and energy storage is an independent energy storage system. The operation method treats new energy and energy storage as two power clusters, each with its own working position on the load diagram. The objects of energy storage regulation and smoothing are the remaining load after deducting the output of new energy from the system load. Energy storage discharges at the peak of the remaining load and charges during the off-peak of the remaining load.

[0089] Regarding the determination of the joint operation mode, in some implementations of this embodiment, the decision-making rules are set as follows: if the wind and solar power output characteristics are wind power output dominance, synchronous changes in new energy output, or only a sharp decrease in wind power output, then the local joint operation mode is selected. Under this joint operation mode, the system thermal power output is lower, while the wind power consumption is more stable and the consumption is improved.

[0090] If the wind and solar power output characteristics are characterized by one of the following: photovoltaic power dominance, asynchronous wind and solar power changes with a decrease in wind power output, simultaneous decrease in wind and solar power output, or only a sudden decrease / increase in photovoltaic power output, then the wide-area joint operation mode is selected. Under this joint operation mode, the system's thermal power output is lower, while the photovoltaic absorption is more stable and the absorption is improved. In addition, the system's maximum thermal power carbon emission intensity is also reduced.

[0091] It should be noted that selecting the local mode in wind-dominated or synchronous wind-solar scenarios, and selecting the wide-area mode in photovoltaic-dominated or asynchronous / sudden wind-solar scenarios, can more accurately adapt to the actual operating needs of the power system. Dynamically selecting the optimal joint operation mode guides energy storage resources to operate in a way that maximizes the overall operating efficiency of the system. This changes the problems of low utilization and poor economic efficiency of the traditional configuration mode, and significantly enhances the value contribution of energy storage assets in supporting the achievement of the dual-carbon goals of the new power system.

[0092] Model building steps:

[0093] In this embodiment, the pre-constructed multi-objective collaborative optimization scheduling model is invoked based on the joint operation mode determined by the mode dynamic selection step. The establishment of this multi-objective collaborative optimization scheduling model is based on the premise of ensuring the balance of power generation and supply of the system, and comprehensively considers the "dual control" objectives of the total carbon emissions and intensity of the system, as well as the scheduling and utilization objectives of different new energy sources and energy storage.

[0094] The commonality between the multi-objective collaborative optimization scheduling models under the two joint operation modes lies in the fact that both models include sub-objectives of minimizing the output of thermal power units and minimizing the maximum carbon emissions of thermal power units. The difference lies in the fact that these two multi-objective collaborative optimization scheduling models also need to reflect the different core objectives of the two joint operation modes respectively.

[0095] In the local joint operation mode, new energy sources and energy storage configured on the same side together form a bundled power unit. Therefore, its core objective is to minimize the fluctuation of the total output of new energy sources and energy storage. In the wide-area joint operation mode, the sum of the output of each new energy source and the independently configured system-level or regional-level energy storage operate as an independent power cluster. Therefore, its core objective is to minimize the fluctuation of the system's remaining load.

[0096] Based on this, in these two multi-objective collaborative optimization scheduling models, the multi-objective collaborative optimization scheduling model corresponding to the local joint operation mode also includes the sub-objective of minimizing the total output fluctuation of new energy and energy storage, and the multi-objective collaborative optimization scheduling model corresponding to the wide-area joint operation mode also includes the sub-objective of minimizing the fluctuation of the system's remaining load.

[0097] Specifically, the expression for the multi-objective collaborative optimization scheduling model corresponding to the local joint operation mode is as follows:

[0098]

[0099] In the above formula, This represents the objective function of the multi-objective collaborative optimization scheduling model. This represents the sub-objective function for the output of the corresponding thermal power unit. This represents the sub-objective function for the maximum carbon emissions of the corresponding thermal power unit. This represents the sub-objective function corresponding to the fluctuation of the total output of new energy sources and energy storage;

[0100] The sub-objective function expression for the output of the corresponding thermal power unit is as follows:

[0101]

[0102] In the above formula, Indicates thermal power unit During the period of efforts, Indicates the total number of thermal power units in the system. This indicates the total number of system optimization scheduling periods. Indicates the number of hours in a time period;

[0103] The sub-objective function expression for the maximum carbon emissions of the corresponding thermal power unit is as follows:

[0104]

[0105] In the above formula, , and The carbon emission intensity coefficient for thermal power units;

[0106] The sub-objective function expression corresponding to the fluctuation of total output of new energy and energy storage is as follows:

[0107]

[0108]

[0109] In the above formula, Indicates the system's new energy and energy storage during the time period Total output This represents the average total output of the system's new energy sources and energy storage. Indicates wind farm station During the period of efforts, Indicates the number of wind farms in the system. Indicates photovoltaic power station During the period of efforts, Indicates the number of photovoltaic power stations. It is a symbolic function with a value of When the value is Time indicates energy storage During the period Discharge, when the value is Time indicates energy storage During the period Charge, Indicates energy storage During the period of efforts, This indicates the total amount of energy stored.

[0110] The multi-objective collaborative optimization scheduling model expression corresponding to the wide-area joint operation mode is as follows:

[0111]

[0112] In the above formula, This represents the sub-objective function corresponding to the fluctuation of the system's remaining load;

[0113] The sub-objective function expression corresponding to the fluctuation of the system's remaining load is as follows:

[0114]

[0115]

[0116] In the above formula, Indicates the system during the time period The remaining load, This represents the average remaining load of the system. Indicates the system during the time period Total load.

[0117] It should be noted that the established multi-objective collaborative optimization scheduling model innovatively introduces the sub-objective of minimizing the maximum carbon emissions of thermal power units, which effectively suppresses the marginal carbon emission deterioration problem caused by deep peak shaving.

[0118] Constraint setting steps:

[0119] In this embodiment, the constraints set for the above multi-objective collaborative optimization scheduling model cover new energy operation constraints, energy storage operation constraints, system operation constraints, and thermal power operation constraints.

[0120] Specifically, the operational constraints for new energy sources mainly target the output range of wind and solar power, and are expressed as follows:

[0121]

[0122]

[0123] In the above formula, Indicates wind farm station The upper limit of output, Indicates photovoltaic power station The upper limit of output;

[0124] Energy storage operation constraints mainly target energy storage discharge, energy storage charging, energy storage capacity, and continuous changes in energy storage capacity. The expression is:

[0125]

[0126]

[0127]

[0128]

[0129] In the above formula, Indicates energy storage The upper limit of output, Indicates energy storage The lower limit of output, Indicates energy storage During the period The amount of stored energy, Indicates energy storage The lower limit of the storage capacity, Indicates energy storage Maximum storage capacity Indicates energy storage The energy conversion efficiency;

[0130] System operation constraints are mainly aimed at power balance, and the expression is:

[0131]

[0132] In addition, for bundled power supplies with electrical physical connections, the power of the outgoing lines or terminals is constrained, as expressed by:

[0133]

[0134] In the above formula, Indicates the sending line or cross-section The lower limit of power, Indicates the sending line or cross-section Downwind farm station During the period of efforts, Indicates the sending line or cross-section Photovoltaic power station During the period of efforts, Indicates the sending line or cross-section energy storage During the period of efforts, Indicates the sending line or cross-section The upper limit of power;

[0135] Thermal power plant operation constraints mainly target the power output of thermal power units, the minimum operating time of thermal power units, the minimum downtime of thermal power units, and the ramp-up capability of thermal power units. The expressions are as follows:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] In the above formula, Indicates thermal power unit The lower limit of output, Indicates thermal power unit The upper limit of output, Indicates thermal power unit During the period Number of consecutive periods that have been running. It is a symbolic function with a value of or When the value is Time indicates thermal power unit During the period When it is in running state, when the value is Time indicates thermal power unit During the period It is currently in a shutdown state. Indicates thermal power unit Minimum number of runtime segments, Indicates thermal power unit During the period The number of consecutive periods of downtime. Indicates thermal power unit Minimum number of downtime periods, Indicates thermal power unit The rate of ascent, Indicates thermal power unit downhill / uphill speed.

[0142] Solution steps:

[0143] In this embodiment, the three-objective optimization problem established by the above-mentioned multi-objective cooperative optimization scheduling model is solved using a genetic algorithm to obtain the Pareto optimal solution set.

[0144] In some specific implementations of this embodiment, the multi-objective cooperative optimization scheduling model is solved using the NSGA-II algorithm, see [link to relevant documentation]. Figure 3 As shown, it includes the following steps:

[0145] Step S1: Generate an initial population in the solution space of the output composition of each type of unit. ;

[0146] Step S2, As a fitness function, for each individual in the population Calculate the corresponding objective function value to evaluate individual fitness;

[0147] Step S3: Calculate the fitness of each individual. The number of dominant individuals and the set of individuals dominated by it are used to rank individuals according to the number of dominant individuals, assigning each individual to the corresponding non-dominated level, and calculating the value of each individual. The degree of crowding in its non-dominated hierarchy;

[0148] Step S4: Optimize the population and generate the parent population through selection, crossover, and mutation operations;

[0149] Step S5: Merge the parent population and the offspring population to obtain a joint population. Perform non-dominated sorting on the joint population, and select individuals according to the non-dominated level and crowding to form a new generation population.

[0150] Step S6: Determine if the maximum number of iterations has been reached. If not, return to step S2. If yes, output the location of the latest generation population in the solution space to obtain the Pareto solution set.

[0151] Furthermore, determining the optimal scheduling and operation scheme based on the Pareto optimal solution set includes the following steps:

[0152] The optimal solutions in the Pareto solution set are evaluated using the ideal optimal point, as shown in the following expression:

[0153]

[0154] In the above formula, Indicates Pareto solution Euclidean distance from the ideal optimal point Indicates Pareto solution Single objective function value, This represents the ideal optimal point, which is the single-objective optimization result for each sub-objective.

[0155] The solution with the minimum Euclidean distance is taken as the optimal scheduling and operation scheme. This optimal scheduling and operation scheme includes the scheduling plans for new energy, energy storage and thermal power in this period. Further execution of this scheduling plan completes the joint operation control of new energy and energy storage in this period.

[0156] It should be noted that the model coordinates and optimizes the system's low-carbon goals and the core operational goals of new energy and energy storage within a unified framework, and achieves an effective trade-off and balance among the three through the NSGA-II algorithm, thus accurately finding the system's overall optimal low-carbon operation. This embodiment utilizes the NSGA-II algorithm for efficient solution, which can accurately ensure the optimal balance between low-carbon performance and system stability under different modes.

[0157] Example 2

[0158] This embodiment, based on the technical solution provided in Embodiment 1, provides a control system for the joint operation of new energy and energy storage. This system applies the joint operation control method for new energy and energy storage described in Embodiment 1. (See also...) Figure 4 As shown, the system includes:

[0159] The mode determination module is used to acquire wind and solar power output characteristics and determine the joint operation mode of new energy and energy storage based on the wind and solar power output characteristics. The joint operation mode includes a local joint operation mode and a wide-area joint operation mode.

[0160] The model determination module is used to call the corresponding multi-objective collaborative optimization scheduling model based on the determined joint operation mode. The multi-objective collaborative optimization scheduling model includes sub-objectives of minimizing the output of thermal power units and minimizing the maximum carbon emissions of thermal power units. The multi-objective collaborative optimization scheduling model corresponding to the local joint operation mode also includes the sub-objective of minimizing the fluctuation of the total output of new energy and energy storage. The multi-objective collaborative optimization scheduling model corresponding to the wide-area joint operation mode also includes the sub-objective of minimizing the fluctuation of the system's remaining load.

[0161] The solution module is used to solve the multi-objective cooperative optimization scheduling model using the NSGA-II algorithm to obtain the Pareto optimal solution set, and to determine the optimal scheduling operation scheme based on the Pareto optimal solution set.

[0162] The functions of each module of the new energy and energy storage joint operation control system in this embodiment are the same as those in the embodiment of the new energy and energy storage joint operation control method, and the technical effects are the same, so they will not be repeated here.

[0163] Example 3

[0164] This embodiment is based on the technical solution provided in Embodiment 1, and provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the new energy and energy storage joint operation control method as described in Embodiment 1.

[0165] Example 4

[0166] This embodiment is based on the technical solution provided in Embodiment 1, and provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the new energy and energy storage joint operation control method as described in Embodiment 1.

[0167] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for controlling joint operation of a new energy source and an energy storage device, characterized in that, The method comprises the following steps: Obtaining wind and light output characteristics, determining a new energy and energy storage joint operation mode based on the wind and light output characteristics, the joint operation mode comprising a local joint operation mode and a wide-area joint operation mode; Calling a corresponding multi-objective collaborative optimization scheduling model based on the determined joint operation mode, the multi-objective collaborative optimization scheduling model comprising a sub-objective of minimizing thermal power unit output and a sub-objective of minimizing maximum carbon emission of the thermal power unit, wherein the multi-objective collaborative optimization scheduling model corresponding to the local joint operation mode further comprises a sub-objective of minimizing total output fluctuation of new energy and energy storage, and the multi-objective collaborative optimization scheduling model corresponding to the wide-area joint operation mode further comprises a sub-objective of minimizing system residual load fluctuation, the total output fluctuation of new energy and energy storage being the degree of deviation of total output of all wind farms, photovoltaic power stations and energy storage power stations within a set period, and the system residual load fluctuation being the degree of deviation of the residual load after deducting wind and light power generation within the set period; Setting a constraint condition and solving the multi-objective collaborative optimization scheduling model by using a genetic algorithm to obtain a Pareto optimal solution set, and determining an optimal scheduling operation scheme based on the Pareto optimal solution set.

2. The method of claim 1, wherein, The determination of the joint operation mode comprises the following steps: Obtaining wind and light output characteristics, and determining to select the local joint operation mode if the wind and light output characteristics show one of wind power output dominance, new energy output synchronous change and only wind power output sudden decrease, and determining to select the wide-area joint operation mode if the wind and light output characteristics show one of photovoltaic output dominance, wind and light asynchronous change and wind power output decrease, wind and light synchronous sudden decrease, and only photovoltaic output sudden decrease / increase.

3. The method of claim 1, wherein, The multi-objective collaborative optimization scheduling model corresponding to the local joint operation mode has the following expression: In the above formula, a target function representing a multi-target collaborative optimization scheduling model, a sub-target function corresponding to the output of a thermal power unit, a sub-target function corresponding to the maximum carbon emission of a thermal power unit, a sub-target function corresponding to the total output fluctuation of new energy and energy storage. The sub-objective function expression corresponding to the thermal power unit output is as follows: In the above formula, represents the thermal power unit In the time period output, represents the total number of system thermal power units, represents the total number of system optimization scheduling time periods, represents the number of hours in the time period; The sub-objective function expression corresponding to the maximum carbon emission of the thermal power unit is as follows: In the above formula, , and is the carbon emission intensity coefficient of the thermal power generating unit; The sub-objective function expression corresponding to the total output fluctuation of new energy and energy storage is as follows: In the above formula, represents the total output of the system new energy and energy storage in the time period , represents the average of the total output of the system new energy and energy storage, represents the output of the wind power station in the time period , represents the number of system wind power stations, represents the output of the photovoltaic power station in the time period , represents the number of photovoltaic power stations, is a symbol function, and the value is when the value is , it represents that the energy storage discharges in the time period , and when the value is , it represents that the energy storage charges in the time period , represents the output of the energy storage in the time period , represents the total number of energy storages.

4. The method of claim 3, wherein the new energy and energy storage combined operation control method is characterized by, The multi-objective collaborative optimization scheduling model corresponding to the wide-area joint operation mode has the following expression: In the above formula, represents a sub-objective function corresponding to the system residual load fluctuation; The sub-objective function expression corresponding to the system residual load fluctuation is as follows: In the above formulae, denotes the system's remaining load at time period , denotes the system's remaining load average, denotes the system's total load at time period .

5. The new energy and energy storage combined operation control method according to any one of claims 3 to 4, characterized in that, The constraint condition comprises: The new energy operation constraint has the following expression: In the above formulae, denotes the upper limit of the output of a wind power plant denotes the upper limit of the output of a wind power plant denotes the upper limit of the output of a photovoltaic power plant denotes the upper limit of the output of a photovoltaic power plant The energy storage operation constraint has the following expression: In the above formula, represents the upper limit of the output of the energy storage represents the lower limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the lower limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the upper limit of the output of the energy storage represents the upper limit of the output of the energy storage The system operation constraint has the following expression: In the above formula, Indicates the sending line or cross-section The lower limit of power, Indicates the sending line or cross-section Downwind farm station During the period of efforts, Indicates the sending line or cross-section Photovoltaic power station During the period of efforts, Indicates the sending line or cross-section energy storage During the period of efforts, Indicates the sending line or cross-section The upper limit of power; The thermal power operation constraint has the following expression: In the above formula, denotes the lower limit of the output of the thermal power unit , denotes the upper limit of the output of the thermal power unit , denotes the upper limit of the output of the thermal power unit , denotes the number of time periods in which the thermal power unit has been continuously running, is a sign function, and has a value of or , when the value is , it indicates that the thermal power unit is in a running state, and when the value is , it indicates that the thermal power unit is in a shutdown state, denotes the minimum running time period of the thermal power unit , denotes the number of time periods in which the thermal power unit has been continuously shut down, denotes the minimum shutdown time period of the thermal power unit , denotes the upper ramp-up rate of the thermal power unit , denotes the lower ramp-up rate of the thermal power unit , denotes the lower ramp-up rate of the thermal power unit .

6. The method of claim 1, wherein, The solution of the multi-objective collaborative optimization scheduling model adopts an NSGA-II algorithm, comprising the following steps: Step S1, generating an initial population in a solution space of the output composition of each type of unit ; Step S2, calculating As a fitness function, the objective function value of each individual in the population is calculated The individual fitness is evaluated by calculating the corresponding objective function value; Step S3, calculating the dominance number of each individual and the set of individuals dominated by it, non-dominant sorting according to the dominance number, classifying the individuals into corresponding non-dominant levels, and calculating the crowding degree of each individual in the non-dominant level where it is located. Step S3, calculating the dominance number of each individual and the set of individuals dominated by it, non-dominant sorting according to the dominance number, classifying the individuals into corresponding non-dominant levels, and calculating the crowding degree of each individual in the non-dominant level where it is located. Step S3, calculating the dominance number of each individual and the set of individuals dominated by it, non-dominant sorting according to the dominance number, classifying the individuals into corresponding non-dominant levels, and calculating the Step S4, generating a parent population by selection, crossover and mutation operations; Step S5, merging the parent population and a child population to obtain a joint population, performing non-dominated sorting on the joint population, and performing individual selection according to the non-dominated level and crowding degree to form a new generation population; Step S6, determining whether the maximum iteration number is reached, if not, returning to step S2, and if yes, outputting the solution space position of the latest generation population to obtain a Pareto solution set.

7. The method of claim 6, wherein the new energy and energy storage combined operation control method is characterized by, The determination of the optimal scheduling operation scheme comprises the following steps: Evaluating the optimal solution in the Pareto solution set by using an ideal optimal point, and the expression is as follows: In the above formula, denotes the Pareto solution Euclidean distance from the ideal optimal point, denotes the Pareto solution single objective function value, denotes the ideal optimal point, which is the single objective optimization result of each sub-objective; The solution with the minimum Euclidean distance is taken as the optimal scheduling operation scheme.

8. A new energy and energy storage combined operation control system, characterized in that, The application is applied to the new energy and energy storage combined operation control method and system in any one of claims 1 to 7, and the system comprises: A mode determination module is configured to obtain wind and light output characteristics, and determine a new energy and energy storage combined operation mode based on the wind and light output characteristics, wherein the combined operation mode comprises a local combined operation mode and a wide-area combined operation mode; A model determination module is configured to call a corresponding multi-objective collaborative optimization scheduling model based on the determined combined operation mode, wherein the multi-objective collaborative optimization scheduling model comprises a sub-target of minimizing thermal power unit output and a sub-target of minimizing maximum carbon emission of the thermal power unit, wherein the multi-objective collaborative optimization scheduling model corresponding to the local combined operation mode further comprises a sub-target of minimizing total new energy and energy storage output fluctuation, and the multi-objective collaborative optimization scheduling model corresponding to the wide-area combined operation mode further comprises a sub-target of minimizing system residual load fluctuation; A solution module is configured to solve the multi-objective collaborative optimization scheduling model by using an NSGA-II algorithm to obtain a Pareto optimal solution set, and determine an optimal scheduling operation scheme based on the Pareto optimal solution set.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the new energy and energy storage combined operation control method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium, and the computer program is executed by the processor to implement the new energy and energy storage combined operation control method in any one of claims 1 to 7.

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