Intelligent dispatching method and system for new energy power generation based on wind-solar-storage integration

Abstract

The application provides a new energy power generation intelligent scheduling method and system based on wind-solar-storage integration. The method comprises the following steps: collecting real-time and predicted output data of wind power generation and photovoltaic power generation, and collecting state of charge and maximum adjustment capacity of an energy storage system to form minimum key operation data for describing uncertainty of new energy output; calculating new energy output change trend index and effective adjustment range based on the data, and constructing a scheduling operation boundary containing fluctuation direction identification and effective adjustment range; discretely generating a candidate scheduling scheme in a predetermined step length within the boundary, and calculating a stability evaluation quantity of each scheme, which comprehensively reflects boundary margin, direction consistency and action sparseness; selecting a scheme with the maximum stability evaluation quantity to generate a final scheduling instruction, and controlling charging and discharging of the energy storage system to stabilize new energy grid-connected power. The application improves the robustness of scheduling decision and grid-connected stability by constructing a scheduling boundary with clear physical constraints and a stability-oriented evaluation mechanism.

Description

Technical Field

[0001] This invention belongs to the field of resource scheduling, and in particular relates to a smart scheduling method and system for new energy power generation based on the integration of wind, solar and energy storage. Background Technology

[0002] With the continuous expansion of installed capacity of new energy power generation, the proportion of new energy sources such as wind power and photovoltaics in the power system is constantly increasing. Their output is significantly affected by meteorological conditions, exhibiting strong randomness, frequent fluctuations, and limited predictability, placing higher demands on the safety and stability of the power grid during grid-connected operation. To mitigate the impact of new energy output fluctuations on grid operation, engineering practice typically involves configuring energy storage systems to form integrated wind-solar-storage power generation units with wind and photovoltaic power generation systems. The rapid response capability of energy storage is used to smooth and support new energy output. In existing technologies, most dispatching methods use power prediction results as the core input, constructing an optimization model that includes economic and stability objectives to obtain the dispatching scheme. During the execution phase, constraint verification or manual intervention is used to handle out-of-bounds issues. However, in actual operation, because new energy output prediction errors are difficult to avoid and can rapidly amplify within a short timescale, dispatching schemes directly generated based on prediction results often deviate from actual operating conditions, leading to frequent constraint conflicts or repeated corrections during the execution phase of dispatching commands. Meanwhile, in multi-time-domain operation scenarios, energy storage systems are easily overused in the early stages of scheduling, weakening their support capabilities under subsequent fluctuations or extreme operating conditions. This results in scheduling strategies nominally meeting optimization objectives but lacking robustness at the overall operational level. The root cause of these problems lies in the fact that existing scheduling methods fail to fully characterize the constraint relationship between the uncertainty of new energy output and the adjustability of energy storage during the scheduling decision-making stage. Consequently, the scheduling scheme is not inherently limited to the range within which the system can operate stably when it is generated. Summary of the Invention

[0003] The purpose of this invention is to design a smart dispatching method and system for new energy power generation based on the integration of wind, solar and energy storage, which can improve the stability of new energy grid connection while taking into account the overall operating efficiency of the system, and enable energy storage regulation capacity to be allocated and utilized more rationally.

[0004] To achieve the above objectives, a first aspect of the present invention provides a smart dispatching method for new energy power generation based on wind-solar-storage integration, the method comprising: Collect real-time power output data of wind power generation systems and photovoltaic power generation systems during the current scheduling cycle and predicted power output data for the next scheduling cycle, and calculate the trend index of new energy power output change; Collect the current state of charge and the current maximum allowable regulation capacity of the energy storage subsystem; The fluctuation direction identifier is determined based on the sign of the new energy output change trend indicator, and the effective adjustment range of the current scheduling cycle is calculated by combining the state of charge, the maximum regulation capacity and the reference output level, and a scheduling operation boundary including the effective adjustment range and the fluctuation direction identifier is constructed. Within the scheduling operation boundary, the interval from zero to the effective adjustment range is discretized with a predetermined step size to obtain a set of energy storage adjustment command ranges. Each energy storage adjustment command range corresponds to a candidate scheduling scheme, and the action direction of each candidate scheduling scheme is consistent with the fluctuation direction identifier. For each candidate scheduling scheme, a stability evaluation value is calculated. The stability evaluation value reflects the margin of the adjustment action from the scheduling operation boundary, the consistency between the adjustment direction and the fluctuation direction of the new energy output, and the sparsity of the adjustment action amplitude. The energy storage regulation command amplitude corresponding to the candidate scheduling scheme with the largest stability assessment value is selected as the final scheduling command. The energy storage subsystem is controlled to perform charging and discharging operations according to the final scheduling instruction.

[0005] Furthermore, the new energy output change trend index is obtained by dividing the difference between the predicted output value of the next scheduling cycle and the real-time output value of the current scheduling cycle by the reference output level, where the reference output level is a benchmark output level that matches the current operating state.

[0006] Furthermore, the calculation of the effective adjustment range introduces a bidirectional adjustment coefficient, which is determined based on the current state of charge of the energy storage subsystem and is used to characterize the difference in the adjustment capability of the energy storage subsystem under different states of charge.

[0007] Furthermore, the predetermined step size is 5% to 20% of the effective adjustment range.

[0008] Furthermore, the calculation of the stability assessment quantity includes a boundary margin term, which is the difference between the effective adjustment range and the energy storage adjustment command range corresponding to the candidate scheduling scheme.

[0009] Furthermore, the calculation of the stability assessment quantity includes a directional consistency term, the value of which is determined based on whether the sign of the new energy output change trend indicator is consistent with the sign of the energy storage adjustment command amplitude corresponding to the candidate scheduling scheme.

[0010] Furthermore, the calculation of the stability evaluation quantity includes a sparse regularization term, which penalizes the magnitude of the energy storage regulation command corresponding to the candidate scheduling scheme in order to suppress excessive regulation actions.

[0011] Furthermore, when the final scheduling instruction is positive, the energy storage subsystem is controlled to perform a charging operation to absorb the power fluctuations caused by the increase in new energy output; when the final scheduling instruction is negative, the energy storage subsystem is controlled to perform a discharging operation to compensate for the power gap caused by the decrease in new energy output.

[0012] Furthermore, the real-time output data of the wind power generation system is obtained by the wind turbine controller through the station control unit, while the real-time output data of the photovoltaic generation system is obtained by the photovoltaic inverter or inverter group control device.

[0013] In a second aspect, the present invention provides a smart dispatching system for new energy power generation based on wind-solar-storage integration, the system comprising: The data acquisition module is used to collect real-time output data of the wind power generation system and photovoltaic power generation system during the current scheduling cycle and predicted output data for the next scheduling cycle, and to collect the current state of charge and the current maximum allowable adjustment capacity of the energy storage subsystem, so as to obtain the minimum key operating data that characterizes the uncertainty of the current new energy output. The scheduling operation boundary construction module is used to determine the fluctuation direction identifier based on the sign of the new energy output change trend indicator, and calculate the effective adjustment range of the current scheduling cycle in combination with the state of charge, the maximum regulation capacity and the reference output level, and construct a scheduling operation boundary that includes the effective adjustment range and the fluctuation direction identifier. The scheduling scheme evaluation module is used to discretize the interval from zero to the effective adjustment range within the scheduling operation boundary with a predetermined step size to obtain a set of energy storage adjustment command ranges. Each energy storage adjustment command range corresponds to a candidate scheduling scheme, and the action direction of each candidate scheduling scheme is consistent with the fluctuation direction identifier. For each candidate scheduling scheme, a stability evaluation quantity is calculated. The stability evaluation quantity reflects the margin of the adjustment action from the scheduling operation boundary, the consistency of the adjustment direction with the fluctuation direction of the new energy output, and the sparsity of the adjustment action range. The scheduling instruction execution module is used to select the energy storage adjustment instruction amplitude corresponding to the candidate scheduling scheme with the largest stability evaluation value as the final scheduling instruction; and to control the energy storage subsystem to perform charging and discharging operations according to the final scheduling instruction.

[0014] The beneficial technical effects of the present invention are at least as follows: To address the aforementioned issues, this invention provides a smart dispatching method and system for new energy power generation based on wind, solar, and energy storage integration. Its core lies in bringing the constraints of new energy output uncertainty and energy storage regulation capabilities forward to the dispatching decision generation stage. By constructing a dispatching operation boundary reflecting the fluctuation characteristics of new energy, and distinguishing the stability of dispatching schemes within this boundary, the final dispatching instructions are executable and operationally robust from the outset. This invention characterizes the real-time output and changing trends of wind and solar power, and combines this with the actual regulation capabilities of the energy storage system under the current operating state, forming a dispatching constraint boundary that can be adjusted according to changes in operating state. This ensures that the generation process of the dispatching scheme is always controlled within the system's truly tolerable operating range. Based on this, by comparing the stability of different regulation actions within the dispatching boundary, it clearly distinguishes dispatching behaviors that are more conducive to maintaining grid connection stability under new energy fluctuation conditions, thus providing a direct and clear decision-making basis for the dispatching execution stage. Through the above technical approach, this invention avoids the problem of frequent modifications to the scheduling scheme during the execution phase, enabling a more rational allocation and utilization of energy storage regulation capacity. While improving the stability of new energy grid connection, it also takes into account the overall system operating efficiency, providing an engineering-feasible and operationally robust implementation path for integrated wind, solar, and energy storage scheduling under conditions of high proportion of new energy access. Attached Figure Description

[0015] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0016] Figure 1 This is a flowchart of the intelligent dispatching method for new energy power generation based on the integration of wind, solar and energy storage, as described in this invention.

[0017] Figure 2 This is a framework diagram of the intelligent dispatching system for new energy power generation based on the integration of wind, solar and energy storage, as described in this invention. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] In one or more embodiments, such as Figure 1 As shown, a smart dispatching method for new energy power generation based on wind, solar, and energy storage integration is disclosed. The method includes the following: S1: Collect real-time power output data of the wind power generation subsystem and photovoltaic power generation subsystem for the current scheduling cycle and predicted power output data for the next scheduling cycle, calculate the trend index of new energy power output change; collect the current state of charge and the current maximum allowable regulation capacity of the energy storage subsystem. Specifically, before the integrated wind, solar, and energy storage power generation dispatch begins, a set of basic operational data needs to be established to accurately reflect the fluctuation characteristics of renewable energy output and the energy storage's regulation capabilities. This step focuses on the core engineering issues of the dispatch phase: whether there are significant trends in wind and solar power output during the current and adjacent dispatch cycles, and how much regulation capacity the energy storage system can provide under the current conditions. Therefore, key operational information is obtained from the existing on-site control system and this information is then systematically organized.

[0020] In practice, real-time output data for wind power generation systems and photovoltaic (PV) power generation systems are collected by the site-level monitoring and control system. For wind farms, this data is typically aggregated by the wind turbine controllers and then sent up by the site control unit, reflecting the combined output of all wind turbines operating on the grid during the current scheduling cycle. For PV power plants, this data is typically collected by the PV inverters or inverter group control devices, reflecting the actual power generation level of the PV array at the current moment. This real-time output data is updated at the scheduling cycle level and serves as a direct basis for describing the current operating status of the new energy source.

[0021] Simultaneously, the predicted power output for the next scheduling cycle is obtained from the short-term power prediction module at the same power station. This prediction module is typically deployed in wind or solar power plants to serve existing scheduling or operation management needs. Its output reflects the judgment on the power output trend for the next scheduling cycle based on current meteorological conditions and operating status. For example, in a solar power plant, when clouds are moving rapidly, the prediction module may indicate a significant decrease in power output in the next scheduling cycle; in a wind farm, when wind speeds are close to operating limits or there is a risk of gusts, the prediction module may indicate significant uncertainty in power output. These prediction results are not used for precise power generation control, but rather to characterize "whether there is a possibility of power output changes requiring energy storage intervention."

[0022] To enable assessment of renewable energy power plants with varying installed capacities and operational levels within a unified dispatch framework, this step transforms the real-time and predicted values ​​of renewable energy output into an output change trend index. This index describes the relative change in renewable energy output between adjacent dispatch cycles, thus avoiding the scale differences caused by directly using absolute output values. The output change trend index is calculated as follows: ; in, This indicator represents the trend of new energy output changes and is used to reflect the direction and magnitude of changes in wind power or photovoltaic power generation relative to the current dispatch cycle in the next dispatch cycle. This indicates the predicted power output value for the next scheduling cycle, which is output by the short-term power prediction module on the power station side. This represents the real-time power output value within the current scheduling cycle, which is collected by the wind power control system or photovoltaic inverter. This represents the reference output level, used to normalize output changes. The reference value is selected as a benchmark output level that matches the current operating status, so that the output change trends of different stations can be compared on the same order of magnitude.

[0023] While acquiring the trend of renewable energy output changes, the current operating status information of the energy storage units is collected from the energy storage subsystem. This information is typically provided by the energy storage management system, including the current state of charge (SOC) of the energy storage units and the allowable charge / discharge regulation capacity under current operating conditions. This data reflects the actual capacity of energy storage to absorb excess renewable energy output or compensate for insufficient renewable energy output within the current dispatch cycle. For example, when an energy storage unit is at a high SOC, its capacity to absorb increased photovoltaic output will be limited; when an energy storage unit is at a low SOC, its capacity to compensate for decreased wind power output will be limited. These constraints directly affect the determination of subsequent dispatch operation boundaries and therefore need to be explicitly included at the beginning of the dispatch phase. After completing the above data collection and processing, the renewable energy output change trend indicators are combined with the current adjustable state of the energy storage subsystem to form an integrated wind-solar-storage operation dataset.

[0024] S2: Determine the fluctuation direction identifier based on the sign of the new energy output change trend indicator, and calculate the effective adjustment range of the current scheduling cycle by combining the state of charge, the maximum regulation capacity and the reference output level, and construct a scheduling operation boundary that includes the effective adjustment range and the fluctuation direction identifier; Specifically, this step transforms the integrated wind-solar-storage operation dataset obtained in the previous step into an "executable scheduling operation boundary." In the integrated wind-solar-storage scenario, the fluctuations in wind and solar power are not directly equivalent to the amount of regulation that energy storage needs to undertake, and the current adjustability of energy storage is not equivalent to the long-term stable adjustment space it can provide. This step binds the trend indicators of renewable energy output changes with the current state of energy storage to the same set of boundary expressions, so that the subsequent joint scheduling scheme is developed around the "intensity of fluctuations that can be absorbed or compensated by energy storage" during the generation stage, thereby ensuring that the scheduling scheme is consistent with the on-site executable capability. The input is the integrated wind-solar-storage operation dataset output from step one, which includes the trend indicators of renewable energy output changes. Reference output level The maximum allowable regulation capacity of the energy storage subsystem under current conditions. and the state of charge of the energy storage subsystem. .in, , The combined calculation results are derived from real-time wind / solar power output and short-term forecasts. and Output by the energy storage management system during the current scheduling cycle. This reflects the current permissible charge / discharge regulation capability. It reflects the relative position of currently available energy.

[0025] In terms of implementation, firstly This serves as a criterion for determining the "direction of fluctuation," enabling the scheduling boundary to distinguish between two scenarios: "output increases primarily rely on charging absorption" and "output decreases primarily rely on discharging compensation." For example, when a photovoltaic power station is located at the edge of a cloud cluster, and short-term forecasts indicate a rapid decrease in output in the next cycle, at this time... If the value is negative, the dispatch boundary needs to highlight the available space for discharge compensation; when the wind farm predicts an increase in output in the next cycle, If positive, the scheduling boundary needs to highlight the available space for charging absorption. This directional information determines the "action focus" of candidate scheduling schemes in subsequent steps, avoiding strategy deviations such as using discharge capacity to absorb rising fluctuations or using charging capacity to compensate for falling fluctuations.

[0026] Subsequently, "predicted fluctuating demand" and "energy storage adjustability" are synthesized on the same scale, and a flexible constraint term related to the state of charge is introduced to characterize the difference in the energy storage's ability to withstand regulation commands under different states of charge. A common phenomenon in engineering is that energy storage can both charge and discharge at a moderate state of charge, with the largest adjustment margin; when energy storage approaches a high or low state of charge, the adjustment margin narrows significantly. To directly reflect this characteristic in the dispatch boundary, this step adopts a method based on... The "bidirectional adjustment coefficient" is used to calculate the effective adjustment range allowed in the current period. : ; in, This indicates the effective adjustment range allowed to absorb or compensate for fluctuations in new energy sources within the current scheduling cycle; The indicator representing the trend of new energy output change comes from the normalized trend quantity constructed in step one from the predicted output and real-time output. This represents the reference output level, derived from the running dataset in step one, used to map the trend quantity to the intensity of adjustment demand at the same scale as scheduling; This represents the maximum allowable regulation capacity of energy storage under the current state, derived from the energy storage management system's output of current operating constraints. This indicates the state of charge of the energy storage system, derived from the energy storage management system. This is a bidirectional adjustment coefficient used to reflect the operational characteristics of energy storage, which has more room for adjustment under moderate charge conditions and naturally narrows its adjustment range under extreme conditions. This expression has direct engineering implications in wind, solar, and energy storage scenarios: when the forecast fluctuates greatly but the energy storage is in an unfavorable charge condition, the boundary will automatically tighten to a range that the energy storage can withstand; when the energy storage is in a favorable charge condition, the boundary allows for more full utilization of the energy storage to absorb or compensate for fluctuations, thereby reducing rapid fluctuations in grid-connected power.

[0027] In obtaining Subsequently, this step organizes the scheduling operation boundary into two quantities that can be directly invoked by subsequent steps: one is the "fluctuation direction indicator" used to limit the direction of the scheme's action, and the other is the "effective adjustment amplitude" used to limit the intensity of the scheme's action. In engineering implementation, the fluctuation direction indicator is directly generated by... The symbol is obtained and with They are encapsulated together as a scheduling execution boundary object. For example, when negative and If the magnitude is small, subsequent steps will prioritize generating candidate solutions that offer "discharge compensation but with limited amplitude" to avoid attempting to fully compensate for predicted decline fluctuations when the available discharge capacity of the energy storage is insufficient; when For positive and The magnitude is relatively large, and subsequent steps will generate candidate schemes that are "primarily based on charging absorption with a relatively sufficient amplitude," making the grid-connected side closer to a smooth output. The output is the boundary of new energy dispatch operation, which includes two quantities: effective adjustment amplitude. With fluctuation direction indicator (by (The sign is determined). This output serves as the input for the next step of generating and evaluating a joint wind-solar-storage scheduling scheme, subjecting the candidate schemes to boundary constraints in both action direction and action intensity.

[0028] S3: Within the scheduling operation boundary, the interval from zero to the effective adjustment range is discretized with a predetermined step size to obtain a set of energy storage adjustment command ranges. Each energy storage adjustment command range corresponds to a candidate scheduling scheme, and the action direction of each candidate scheduling scheme is consistent with the fluctuation direction identifier. For each candidate scheduling scheme, a stability evaluation quantity is calculated. The stability evaluation quantity reflects the margin of the adjustment action from the scheduling operation boundary, the consistency of the adjustment direction with the fluctuation direction of the new energy output, and the sparsity of the adjustment action range. Specifically, within the renewable energy dispatching operation boundary given in the previous stage, this step generates a set of executable candidate schemes for joint wind, solar, and energy storage dispatching, and outputs evaluation results for dispatching schemes that differentiate between those with "more stable renewable energy grid connection." The previous stage has already compressed the renewable energy output change trend and the energy storage adjustability into two direct constraints: the effective adjustment range. With fluctuation direction indicator (by (The sign of the constraint is determined). This step transforms these two constraints into rules for generating candidate solutions and evaluates the most typical engineering contradictions of wind-solar-storage systems: the closer the energy storage regulation is to the boundary, the easier it is to trigger reverse regulation when the prediction deviation or fluctuation is amplified, causing the grid-connected power to "oscillate back and forth" between adjacent cycles; if the energy storage regulation retains a certain margin, it can absorb prediction errors, reduce command jitter, and make the grid-connected side more stable. The output of this step is used to allow the subsequent scheduling execution stage to directly select the "more stable" scheduling action, instead of arbitrarily selecting it within the boundary. Our input is the new energy scheduling operation boundary output from step two, which includes... Indicator of the direction of fluctuation. Based on step two , , , The calculation yields the maximum allowable effective adjustment range for the current period; the direction of fluctuation is indicated by... The symbol is obtained and used to define the direction of the scheduling action in this cycle as absorbing upward fluctuations or compensating for downward fluctuations.

[0029] In implementation, candidate scheduling schemes are generated using a "discrete intensity within the boundary" approach to avoid enumerating complex continuous control sequences. First, the sign direction of the candidate scheduling action is determined based on the fluctuation direction identifier, and then... A candidate set is formed by selecting several representative strength levels. The selection of these levels can adopt a fixed proportion according to engineering practices, for example, taking... Several proportional points constitute the candidate adjustment command amplitude This allows for the formation of multiple executable wind-solar-storage joint scheduling candidates within the same scheduling cycle. For example, when the photovoltaic output forecast shows a downward trend and... The smaller the range, the more concentrated the candidate set will be at the small discharge compensation level; when the wind power output forecast shows an upward trend and The candidate set is relatively large, covering charging absorption levels from small to large. Since the candidate levels are all based on... The upper limit is set, so the candidate schemes are naturally subject to boundary constraints and will not produce energy storage actions that exceed the limit.

[0030] After constructing the candidate set, a stability evaluation quantity is calculated for each candidate scheduling scheme. This evaluation quantity directly addresses the engineering goals of wind, solar, and energy storage grid connection: on the one hand, it requires that the adjustment actions not be too close to the boundary to retain a buffer space for prediction errors; on the other hand, it requires that the adjustment actions not induce reverse adjustment demands in adjacent scheduling cycles, i.e., avoiding the oscillating behavior of "excessive charging absorption in this cycle leading to the need for discharge compensation in the next cycle". To this end, this step constructs the stability evaluation quantity as a combination of two terms: a boundary margin term and a directional consistency term, and introduces a sparse regularization term for the new energy scheduling scenario, so that the scheduling command in the candidate set tends to select "few and stable" action intensities, thereby reducing frequent switching at the execution end. The stability evaluation quantity is calculated as follows: ; in, This indicates the stability assessment result of the candidate scheduling scheme. The larger the value, the more likely the scheme is to maintain stable grid-connected operation under conditions of renewable energy fluctuations. The magnitude of the energy storage regulation command corresponding to the candidate scheduling scheme is directly given by the candidate level generation process; The effective adjustment range output from step two is used as the boundary constraint benchmark. The indicator representing the trend of new energy output change constructed in step one has a sign indicating the direction of fluctuation. This represents a symbolic mapping operator used to... and Mapped to direction identifier; The sparse regular weights are preset by the scheduling strategy and are used to suppress unnecessary excessive adjustment actions in the candidate set, making the scheduling instructions more inclined to adopt schemes with moderate amplitude, thereby reducing the risk of grid-connected power fluctuations caused by frequent switching of energy storage actions.

[0031] This assessment method has directly interpretable engineering implications in wind, solar, and energy storage scenarios. Boundary margin term. This reflects the margin for adjusting the distance from the boundary of the action. The larger the margin, the lower the risk of the system being more sensitive to prediction deviations; directional consistency term. This term reflects whether the adjustment direction is consistent with the direction of new energy fluctuations. When the directions are consistent, this term takes a higher value, ensuring that the scheme prioritizes the correct direction for "absorbing the rise" or "compensating for the fall"; sparse regularization term. This suppresses the tendency to favor "the larger the amplitude, the better" in the candidate set, prompting dispatchers to minimize command intensity while meeting fluctuation regulation needs, thereby reducing power fluctuations on the grid-connected side and the frequency of energy storage switching. For example, when forecasts indicate a decline in photovoltaic output and limited energy storage discharge capacity, the maximum amplitude discharge scheme, while nominally compensating for more of the shortfall, will suffer from insufficient margin and regularization penalties. The low amplitude of the discharge signal leads to the selection of a moderate discharge scheme. When forecasts indicate that wind power output will increase and energy storage is in a suitable charging state, a moderate-amplitude charging scheme often achieves a better balance between margin, directional consistency, and regularization. The higher the score, the more likely it is to be selected.

[0032] In actual deployment, this step calculates the results for each candidate in the set. Based on this, a scheduling scheme evaluation result is generated. This evaluation result is represented by "candidate scheduling scheme identifiers and their corresponding..." The core content is the "value," which can be directly used to select and issue scheduling instructions in the next step. Because... The calculation depends on the output of step two. And the output of step one The determined directional markers ensure that the evaluation results are consistent with the previous steps, avoiding the problem of the evaluation logic being disconnected from the boundary construction logic.

[0033] S4: Select the energy storage regulation command amplitude corresponding to the candidate scheduling scheme with the largest stability evaluation value as the final scheduling command; control the energy storage subsystem to perform charging and discharging operations according to the final scheduling command; Specifically, this step focuses on the execution phase of the integrated wind-solar-storage dispatch process. Its core task is to transform the dispatch scheme evaluation results from the previous stage into control commands that can be directly executed on-site, enabling the energy storage system to participate in renewable energy grid connection regulation according to predetermined stability targets within the current dispatch cycle. The input to this step is the dispatch scheme evaluation results output from the previous step, which are expressed as the magnitude of the energy storage regulation commands corresponding to the candidate dispatch schemes. and its stability assessment quantity The core content. This indicates the magnitude of the adjustment actions that energy storage may take within the current scheduling cycle, and its range is already constrained by the scheduling operation boundaries. Constraints; This indicates the stability level of the adjustment action under conditions of renewable energy fluctuations, and is used to distinguish the stability of different candidate schemes in grid-connected operation.

[0034] In the specific implementation process, the results of the scheduling scheme evaluation are first screened to ensure the stability evaluation quantity. Higher-performing candidate solutions are added to the execution candidate set. In engineering practice, a simple and clear decision rule can be adopted: select the scheduling solution with the highest stability evaluation value from the candidate set as the execution solution for the current scheduling cycle. This selection process can be expressed as: ; in, This indicates the magnitude of the final energy storage regulation command selected for execution in the current scheduling cycle; This represents the adjustment magnitude from the set of candidate scheduling schemes in the previous stage; This represents the stability assessment value corresponding to each adjustment magnitude. Through this rule, the stability assessment results are directly translated into explicit execution actions during the scheduling execution phase, ensuring logical continuity in scheduling decisions.

[0035] In obtaining Next, this step maps it to specific integrated wind-solar-storage dispatch commands and sends them to the field control system via the dispatch communication interface. In engineering implementation, the issuance of dispatch commands is usually accomplished through the communication link between the dispatch master station and the field control system, with the energy storage management system acting as the primary executor, receiving commands containing... The system receives control commands and adjusts the charging and discharging power of the energy storage unit accordingly. When When the value is positive, the energy storage system performs a charging operation to absorb power fluctuations caused by the increase in renewable energy output; when... When the value is negative, the energy storage system performs a discharge operation to compensate for the power gap caused by the decrease in renewable energy output. Because The value of is already constrained by the scheduling operation boundary in the previous steps, and the energy storage system can remain within the allowable operating range during execution without triggering power or energy operation limits.

[0036] During the execution of dispatch instructions, wind and solar power systems operate according to predetermined grid connection control strategies, and their output is not forcibly adjusted. Random fluctuations in renewable energy output are primarily mitigated by the charging and discharging behavior of energy storage systems. This execution method has clear engineering advantages: firstly, it avoids frequent intervention in wind and solar grid connection control strategies; secondly, it allows grid-connected power changes to be handled by fast-responding and highly controllable energy storage systems, resulting in a smoother power curve on the grid side. For example, in scenarios where solar power output rapidly declines due to cloud cover, the energy storage system... It provides compensating power to suppress the magnitude and rate of change of grid-connected power; in scenarios where wind power output rises rapidly due to increased wind speed, the energy storage system... It absorbs excess power, preventing a sharp increase in grid-connected power.

[0037] In one or more embodiments, such as Figure 2 As shown, a smart dispatching system for new energy power generation based on wind, solar, and energy storage integration is disclosed. The system includes: The data acquisition module is used to collect real-time output data of the wind power generation system and photovoltaic power generation system during the current scheduling cycle and predicted output data for the next scheduling cycle, and to collect the current state of charge and the current maximum allowable adjustment capacity of the energy storage subsystem, so as to obtain the minimum key operating data that characterizes the uncertainty of the current new energy output. The scheduling operation boundary construction module is used to determine the fluctuation direction identifier based on the sign of the new energy output change trend indicator, and calculate the effective adjustment range of the current scheduling cycle in combination with the state of charge, the maximum regulation capacity and the reference output level, and construct a scheduling operation boundary that includes the effective adjustment range and the fluctuation direction identifier. The scheduling scheme evaluation module is used to discretize the interval from zero to the effective adjustment range within the scheduling operation boundary with a predetermined step size to obtain a set of energy storage adjustment command ranges. Each energy storage adjustment command range corresponds to a candidate scheduling scheme, and the action direction of each candidate scheduling scheme is consistent with the fluctuation direction identifier. For each candidate scheduling scheme, a stability evaluation quantity is calculated. The stability evaluation quantity reflects the margin of the adjustment action from the scheduling operation boundary, the consistency of the adjustment direction with the fluctuation direction of the new energy output, and the sparsity of the adjustment action range. The scheduling instruction execution module is used to select the energy storage adjustment instruction amplitude corresponding to the candidate scheduling scheme with the largest stability evaluation value as the final scheduling instruction; and to control the energy storage subsystem to perform charging and discharging operations according to the final scheduling instruction.

[0038] It is worth noting that the specific workflow of the intelligent dispatching system for new energy power generation based on wind, solar and energy storage integration provided in this embodiment of the invention is the same as that of the intelligent dispatching method for new energy power generation based on wind, solar and energy storage integration described in the above embodiments, and will not be repeated here.

[0039] This invention also provides a smart dispatching device for new energy power generation based on wind, solar, and energy storage integration, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above embodiments of the smart dispatching method for new energy power generation based on wind, solar, and energy storage integration. Figure 1 The steps S1 to S4 described above; or, when the processor executes the computer program, it implements the functions of each module in the above system embodiments.

[0040] For example, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the intelligent dispatching device for new energy power generation based on wind, solar, and energy storage integration.

[0041] The integrated wind, solar, and energy storage intelligent dispatching device for new energy power generation can be a desktop computer, laptop, handheld computer, or cloud server, etc. This integrated device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the integrated wind, solar, and energy storage intelligent dispatching device may also include input / output devices, network access devices, buses, etc.

[0042] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the wind-solar-storage integrated intelligent dispatching equipment for new energy power generation, connecting all parts of the equipment via various interfaces and lines.

[0043] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the intelligent dispatching equipment for new energy power generation based on wind, solar and energy storage integration by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created according to the operation of the air conditioning controller, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0044] The integrated modules of the intelligent dispatching equipment for new energy power generation based on wind, solar, and energy storage, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0045] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0046] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A smart dispatching method for new energy power generation based on wind, solar, and energy storage integration, characterized in that, The method includes: Collect real-time power output data of wind power generation systems and photovoltaic power generation systems during the current scheduling cycle and predicted power output data for the next scheduling cycle, and calculate the trend index of new energy power output change; Collect the current state of charge and the current maximum allowable regulation capacity of the energy storage subsystem; The fluctuation direction identifier is determined based on the sign of the new energy output change trend indicator, and the effective adjustment range of the current scheduling cycle is calculated by combining the state of charge, the maximum regulation capacity and the reference output level, and a scheduling operation boundary including the effective adjustment range and the fluctuation direction identifier is constructed. Within the scheduling operation boundary, the interval from zero to the effective adjustment range is discretized with a predetermined step size to obtain a set of energy storage adjustment command ranges. Each energy storage adjustment command range corresponds to a candidate scheduling scheme, and the action direction of each candidate scheduling scheme is consistent with the fluctuation direction identifier. For each candidate scheduling scheme, a stability evaluation value is calculated. The stability evaluation value reflects the margin of the adjustment action from the scheduling operation boundary, the consistency between the adjustment direction and the fluctuation direction of the new energy output, and the sparsity of the adjustment action amplitude. The energy storage regulation command amplitude corresponding to the candidate scheduling scheme with the largest stability assessment value is selected as the final scheduling command. The energy storage subsystem is controlled to perform charging and discharging operations according to the final scheduling instruction.

2. The intelligent dispatching method for new energy power generation based on wind-solar-storage integration as described in claim 1, characterized in that, The new energy output change trend index is obtained by dividing the difference between the predicted output value of the next scheduling cycle and the real-time output value of the current scheduling cycle by the reference output level, which is a benchmark output level that matches the current operating state.

3. The intelligent dispatching method for new energy power generation based on wind-solar-storage integration as described in claim 1, characterized in that, The calculation of the effective adjustment range introduces a bidirectional adjustment coefficient, which is determined based on the current state of charge of the energy storage subsystem and is used to characterize the difference in the adjustment capability of the energy storage subsystem under different states of charge.

4. The intelligent dispatching method for new energy power generation based on wind-solar-storage integration as described in claim 1, characterized in that, The predetermined step size is 5% to 20% of the effective adjustment range.

5. The intelligent dispatching method for new energy power generation based on wind-solar-storage integration as described in claim 1, characterized in that, The calculation of the stability assessment quantity includes a boundary margin term, which is the difference between the effective adjustment range and the energy storage adjustment command range corresponding to the candidate scheduling scheme.

6. The intelligent dispatching method for new energy power generation based on wind-solar-storage integration as described in claim 1, characterized in that, The calculation of the stability assessment quantity includes a directional consistency term, the value of which is determined based on whether the sign of the new energy output change trend index is consistent with the sign of the energy storage adjustment command amplitude corresponding to the candidate scheduling scheme.

7. The intelligent dispatching method for new energy power generation based on wind-solar-storage integration as described in claim 1, characterized in that, The calculation of the stability assessment quantity includes a sparse regularization term, which penalizes the magnitude of the energy storage regulation command corresponding to the candidate scheduling scheme in order to suppress excessive regulation actions.

8. The intelligent dispatching method for new energy power generation based on wind-solar-storage integration as described in claim 1, characterized in that, When the final scheduling instruction is positive, the energy storage subsystem is controlled to perform a charging operation to absorb the power fluctuations caused by the increase in new energy output; when the final scheduling instruction is negative, the energy storage subsystem is controlled to perform a discharging operation to compensate for the power gap caused by the decrease in new energy output.

9. The intelligent dispatching method for new energy power generation based on wind-solar-storage integration as described in claim 1, characterized in that, The real-time output data of the wind power generation system is obtained by the wind turbine controller through the station control unit, while the real-time output data of the photovoltaic generation system is obtained by the photovoltaic inverter or inverter group control device.

10. A smart dispatching system for new energy power generation based on wind, solar, and energy storage integration, characterized in that: The system includes: The data acquisition module is used to collect real-time output data of the wind power generation system and photovoltaic power generation system during the current scheduling cycle and predicted output data for the next scheduling cycle, and to collect the current state of charge and the current maximum allowable adjustment capacity of the energy storage subsystem, so as to obtain the minimum key operating data that characterizes the uncertainty of the current new energy output. The scheduling operation boundary construction module is used to determine the fluctuation direction identifier based on the sign of the new energy output change trend indicator, and calculate the effective adjustment range of the current scheduling cycle in combination with the state of charge, the maximum regulation capacity and the reference output level, and construct a scheduling operation boundary that includes the effective adjustment range and the fluctuation direction identifier. The scheduling scheme evaluation module is used to discretize the interval from zero to the effective adjustment range within the scheduling operation boundary with a predetermined step size to obtain a set of energy storage adjustment command ranges. Each energy storage adjustment command range corresponds to a candidate scheduling scheme, and the action direction of each candidate scheduling scheme is consistent with the fluctuation direction identifier. For each candidate scheduling scheme, a stability evaluation quantity is calculated. The stability evaluation quantity reflects the margin of the adjustment action from the scheduling operation boundary, the consistency of the adjustment direction with the fluctuation direction of the new energy output, and the sparsity of the adjustment action range. The scheduling instruction execution module is used to select the energy storage adjustment instruction amplitude corresponding to the candidate scheduling scheme with the largest stability evaluation value as the final scheduling instruction; and to control the energy storage subsystem to perform charging and discharging operations according to the final scheduling instruction.

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