Control methods and devices for multi-element hybrid energy storage coupled with new energy sources

Abstract

This invention relates to a control method and apparatus for multi-element hybrid energy storage coupled with new energy sources. The method includes: constructing a first charge-discharge control model based on short-term power prediction values ​​of the new energy power generation device and frequency parameters of the power system; constructing a second charge-discharge control model based on long-term output power prediction values ​​of the new energy power generation device and peak-valley power parameters of the power system; modifying the parameters of the second charge-discharge control model by combining the output of the first charge-discharge control model to obtain a modified second charge-discharge control model; using the first charge-discharge control model to perform charge-discharge control on a fast electrochemical energy storage device on a short time scale, and using the modified second charge-discharge control model to perform charge-discharge control on a slow physical energy storage device on a long time scale. The technical solution provided by this invention adopts a hierarchical control strategy, solving the problem of fluctuation coordination of new energy power generation at different time scales.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of power system operation and control technology, and in particular to a control method and device for multi-element hybrid energy storage coupled with new energy sources. Background Technology

[0002] Multi-element hybrid energy storage coupled with new energy sources is a comprehensive solution that combines multiple energy storage technologies with new energy power generation systems to improve energy utilization efficiency, stability, and economy. This coupled system can effectively address the intermittency and volatility of new energy sources (such as solar and wind power), enhancing the stability and reliability of the power grid. In the synergistic application of multi-element hybrid energy storage systems and new energy power generation systems, due to the significant spatiotemporal mismatch characteristics of new energy power generation, a single energy storage technology often struggles to simultaneously meet multiple requirements such as rapid power support, energy time-shift regulation, and long-term backup. This difference in technological characteristics necessitates the construction of a composite architecture incorporating multiple forms of energy storage media to achieve dynamic power balance between source, storage, and load.

[0003] However, the nonlinear coupling relationship between energy storage media, the game mechanism between multi-objective optimization parameters, and the interaction between market mechanisms and physical constraints mean that system capacity configuration must take into account both technical feasibility and economic optimization. This requires control strategies to break through the traditional single time constant adjustment mode, which places higher demands on the accuracy of system modeling and the adaptability of control algorithms. How to achieve reasonable planning and coordinated control of multi-element hybrid energy storage systems has become an urgent technical problem to be solved. Summary of the Invention

[0004] Based on the above-mentioned situation of the prior art, the purpose of this invention is to provide a control method and device for multi-element hybrid energy storage coupled with new energy, which improves the accuracy and reliability of control of multi-element hybrid energy storage coupled with new energy.

[0005] To achieve the above objectives, according to a first aspect of the present invention, a control method for multi-element hybrid energy storage coupled with new energy sources is provided, comprising the steps of:

[0006] The first charge-discharge control model is constructed based on the short-time power prediction value of the new energy power generation device and the frequency parameters of the power system.

[0007] A second charge-discharge control model is constructed based on the long-term output power prediction of the new energy power generation device and the power parameters of the power system.

[0008] The parameters of the second charge-discharge control model are corrected by combining the output of the first charge-discharge control model to obtain the corrected second charge-discharge control model.

[0009] A first charge-discharge control model is used to control the charge and discharge of a fast electrochemical energy storage device on a short time scale, while a modified second charge-discharge control model is used to control the charge and discharge of a slow physical energy storage device on a long time scale.

[0010] Furthermore, the first charge-discharge control model includes a first objective function and a first constraint condition; the first objective function J1 is expressed as:

[0011]

[0012] Among them, f ref f is the rated frequency of the power system. t Let P be the actual frequency of the power system at time t. e,t P represents the charge / discharge power of the fast electrochemical energy storage device at time t, where charging is negative and discharging is positive. pv,ts Let t be the short-time power prediction value of photovoltaic power generation at time t, and α be the power fluctuation impact factor of photovoltaic power generation, where α < 0;

[0013] The first constraint includes a first power constraint, a first SOC constraint, and a frequency-power relationship constraint.

[0014] Furthermore, the first power constraint condition is expressed as:

[0015] -P e,max-c ≤P e,t ≤P e,max-d

[0016] The first SOC constraint is expressed as follows:

[0017] SOC min ≤SOC t ≤SOC max

[0018] The frequency-power relationship constraint is expressed as follows:

[0019]

[0020] Among them, P e,max-c and P e,max-d These represent the maximum charging power and maximum discharging power of fast electrochemical energy storage, respectively, and the State of Charge (SOC). t Let SOC be the SOC value of the fast electrochemical energy storage device at time t. min and SOC max , respectively, represent the minimum and maximum SOC values ​​of the fast electrochemical energy storage device, D is the system frequency regulation coefficient, and β is the photovoltaic power generation regulation coefficient.

[0021] Furthermore, the second charge-discharge control model includes a second objective function and a second constraint condition; the second objective function J2 is expressed as:

[0022]

[0023] Among them, C m-c and C m-d P represents the charging cost factor and discharging cost factor for slow physical energy storage devices, respectively. m,t Let t be the charging and discharging power of the slow physical energy storage device, with charging being negative and discharging being positive, and SOC being... m,op For slow physical energy storage devices, the optimal SOC (State of Charge) is... m,t Let δ be the SOC value of the slow physical energy storage device at time t, δ be the power balance weighting coefficient, γ be the influencing factor of the slow physical energy storage charge and discharge efficiency, and P be the SOC value of the device at time t. pv,tl Let t be the long-term power prediction value of photovoltaic power generation at time t;

[0024] The second constraint includes the second power constraint, the second SOC constraint, the grid power balance constraint, and the charge / discharge rate constraint.

[0025] Furthermore, the second power constraint condition is expressed as follows:

[0026] -P m,max-c ≤P m,t ≤P m,max-d

[0027] The second SOC constraint is expressed as follows:

[0028] SOC m-min ≤SOC m,t ≤SOC m-max

[0029] The power balance constraint condition of the power grid is expressed as follows:

[0030] P load,t =P pv,tl +P m,t +P gin,t -P gout,t

[0031] The charge / discharge rate constraint is expressed as follows:

[0032]

[0033] Among them, P m,max-c and P m,max-d These represent the maximum charging power and maximum discharging power of the slow physical energy storage device, respectively, and the State of Charge (SOC). m-min and SOC m-maxThese are the minimum and maximum SOC values ​​for a slow physical energy storage device, respectively. gin,t P is the power input from the power system. gout,t R is the power output to the power system. max This represents the upper limit of the charge / discharge rate for slow physical energy storage devices.

[0034] Furthermore, the parameters of the second charge-discharge control model are corrected based on the output of the first charge-discharge control model to obtain the corrected second charge-discharge control model, including the following steps:

[0035] The parameters of the second charge-discharge control model are corrected according to the predetermined cycle and the output of the first charge-discharge control model.

[0036] Under the first constraint conditions, the first charge-discharge control model is solved to obtain the fast electrochemical energy storage charge-discharge power.

[0037] Calculate the average value of the fast electrochemical energy storage charging and discharging power output by the first charging and discharging control model within the predetermined period, as the average charging and discharging power; calculate the average value of the absolute value of the difference between the rated frequency and the actual frequency of the power system within the predetermined period, as the average frequency deviation.

[0038] The second objective function is modified based on the average charge / discharge power and average frequency deviation.

[0039] Furthermore, the correction of the second objective function based on the average charge / discharge power and average frequency deviation includes:

[0040] The power balance weighting coefficients in the second objective function are corrected based on the average frequency deviation:

[0041] δ=δ0+k1·Δf

[0042] Where δ0 represents the power balance weighting coefficient before correction, δ represents the power balance weighting coefficient after correction, k1 represents the first proportional coefficient, and Δf represents the average frequency deviation.

[0043] Furthermore, the correction of the second objective function based on the average charge / discharge power and average frequency deviation includes:

[0044] The charging cost coefficient and discharging cost coefficient in the second objective function are corrected based on the average charging and discharging power:

[0045]

[0046] Among them, C m-c0 C represents the uncorrected charging cost factor. m-c C represents the corrected charging cost coefficient. m-d0C represents the discharge cost factor before correction. m-d This represents the corrected discharge cost coefficient. k1 represents the average charge / discharge power, k2 represents the second proportional coefficient, and k3 represents the third proportional coefficient.

[0047] According to another aspect of the present invention, a control device for multi-element hybrid energy storage coupled with new energy sources is provided, comprising:

[0048] The first charge-discharge control model construction module is used to construct the first charge-discharge control model based on the short-time power prediction value of the new energy power generation device and the frequency parameters of the power system.

[0049] The second charge-discharge control model construction module is used to construct the second charge-discharge control model based on the long-term output power prediction value of the new energy power generation device and the power parameters of the power system.

[0050] The parameter correction module is used to correct the parameters of the second charge-discharge control model by combining the output of the first charge-discharge control model, so as to obtain the corrected second charge-discharge control model.

[0051] The charge / discharge control module is used to control the charge / discharge of a fast electrochemical energy storage device on a short time scale using a first charge / discharge control model, and to control the charge / discharge of a slow physical energy storage device on a long time scale using a modified second charge / discharge control model.

[0052] In summary, this invention provides a control method and apparatus for multi-element hybrid energy storage coupled with new energy sources. The control method includes: constructing a first charge-discharge control model based on the short-term power prediction value of the new energy power generation device and the frequency parameters of the power system; constructing a second charge-discharge control model based on the long-term output power prediction value of the new energy power generation device and the power parameters of the power system; correcting the parameters of the second charge-discharge control model by combining the output of the first charge-discharge control model to obtain a corrected second charge-discharge control model; using the first charge-discharge control model to perform charge-discharge control on a fast electrochemical energy storage device on a short time scale, and using the corrected second charge-discharge control model to perform charge-discharge control on a slow physical energy storage device on a long time scale. The technical solution provided by this invention adopts a hierarchical control strategy, which solves the problem of fluctuation coordination of new energy power generation at different time scales. Through hybrid models and dynamic correction, it compensates for the control deviations that may be caused by inaccurate model predictions, while ensuring the consistency of the hierarchical control strategy, thereby providing an efficient and reliable energy storage control solution for new energy power generation systems. Attached Figure Description

[0053] Figure 1 This is a flowchart of a control method for multi-element hybrid energy storage coupled with new energy sources provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0055] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or slow physical connections, but can include electrical connections, whether direct or indirect.

[0056] This invention provides a control method for a multi-element hybrid energy storage coupled with new energy sources. This control method is used in a multi-element hybrid energy storage coupled with new energy system, which includes a new energy power generation device, a fast electrochemical energy storage device, a slow physical energy storage device, and a control device. In new energy power generation systems (such as photovoltaic power generation systems), distributed photovoltaic power generation is connected to the grid. Due to unstable environmental factors such as sunlight, the power generation may fluctuate greatly. The control method provided in this invention uses multi-element hybrid energy storage to regulate the output of photovoltaic power generation. It utilizes fast electrochemical energy storage for short-term frequency support and slow physical energy storage for long-term power regulation, thereby enabling distributed energy sources to be stably integrated into the grid and improving the reliability of power supply. Among them, new energy power generation devices are photovoltaic power generation devices; fast electrochemical energy storage devices are, for example, battery packs composed of lithium-ion battery cells. Fast electrochemical energy storage has the characteristics of fast response and high energy density, and is used for photovoltaic power generation output regulation on short time scales (e.g., 15 minutes to 2 hours); slow physical energy storage devices are, for example, compressed air energy storage. Slow physical energy storage is suitable for large-scale energy storage and long-term energy storage, and is used for photovoltaic power generation output regulation on long time scales (e.g., more than 24 hours). Figure 1 The flowchart of this control method is shown in the figure. Figure 1 As shown, the method includes the following steps:

[0057] S202. A first charge-discharge control model is constructed based on the short-time power prediction value of the new energy power generation device and the frequency parameters of the power system. In this embodiment of the invention, the new energy power generation device is a photovoltaic cell. The short-time power prediction value is obtained by combining a physical model with a Gaussian process regression (GPR) model. The theoretical photovoltaic power generation is calculated using the physical model, and then the theoretical photovoltaic power generation is combined with the GPR model to perform short-time power prediction. The frequency parameters of the power system include parameters such as the rated frequency of the power system and the actual operating frequency of the power system.

[0058] In the calculation of short-time power prediction, an improved single-diode model can be used as the physical model. The formula for calculating the theoretical photovoltaic power generation is as follows:

[0059]

[0060] Among them, P theo I represents the theoretical photovoltaic power generation capacity. ph Represents the photogenerated current, which is proportional to the light intensity; I0 represents the reverse saturation current; q represents the electron charge; V represents the photovoltaic cell terminal voltage; I represents the photovoltaic cell output current; R s R represents the equivalent series resistance. sh Let A represent the equivalent parallel resistance, K represent the diode quality factor, K represent the Boltzmann constant, and T represent the photovoltaic cell temperature. Historical measured power and corresponding meteorological data (including irradiance and temperature) are obtained from historical data. The difference between historical measured power and theoretical photovoltaic power generation is used as the training objective of the GPR model. The GPR model is trained using the training set data to learn the deviation pattern between measured and theoretical photovoltaic power generation. The hyperparameters of the model are estimated by maximizing the log-marginal likelihood function, resulting in a well-trained GPR model. During prediction, the theoretical photovoltaic power generation under the current meteorological data is first obtained through the physical model. Then, the same feature information is input into the trained GPR model to obtain the prediction deviation value. The two are added together to obtain the final short-term power prediction value.

[0061] In this embodiment of the invention, a combination of physical models and machine learning models (such as GPR models) is used to obtain short-term photovoltaic power output predictions. The physical model provides basic trend predictions, while the machine learning model has strong flexibility, especially when weather changes suddenly or environmental conditions change rapidly. It can quickly adapt to new data and correct the deviations of the physical model, thereby improving the adaptability and accuracy of the prediction results to short-term environmental changes.

[0062] The first charge-discharge control model includes a first objective function and first constraints. In this embodiment of the invention, the first objective function J1 is expressed as:

[0063]

[0064] Among them, f ref f is the rated frequency of the power system. t Let P be the actual frequency of the power system at time t. e,t P represents the charge / discharge power of the fast electrochemical energy storage device at time t, where charging is negative and discharging is positive. pv,ts Let t be the short-term power prediction value of photovoltaic power generation at time t, and α be the power fluctuation factor of photovoltaic power generation, where α < 0.

[0065] The first constraint conditions include the first power constraint condition, the first SOC constraint condition, and the frequency-power relationship constraint condition, which are expressed as follows:

[0066] -P e,max-c ≤P e,t ≤P e,max-d

[0067] SOC min ≤SOC t ≤SOC max

[0068]

[0069] Among them, P e,max-c and P e,max-d These represent the maximum charging power and maximum discharging power of fast electrochemical energy storage, respectively, and the State of Charge (SOC). t The SOC value of the fast electrochemical energy storage device at time t can be obtained using existing conventional methods. min and SOC max These are the minimum and maximum SOC values ​​for the fast electrochemical energy storage device, respectively. D is the system frequency regulation coefficient, which can be set based on system inertia and the allowable frequency deviation range. β is the photovoltaic power generation regulation coefficient, which can be set according to the relationship between frequency deviation and required energy storage power regulation under different operating conditions, as well as the characteristics of different energy storage devices such as charging and discharging response speed and power regulation accuracy. Its value is usually between 0 and 1.

[0070] The first objective function described above considers the grid frequency stability requirements and the impact of photovoltaic power generation fluctuations on energy storage control. Fast electrochemical energy storage can adjust charging and discharging power promptly when photovoltaic power generation changes rapidly, thereby reducing the impact of frequency fluctuations on the grid. The photovoltaic power generation fluctuation impact factor α can be set based on historical data analysis of the correlation between the rate of change of photovoltaic power generation and system frequency fluctuations, as well as the charging and discharging status of fast electrochemical energy storage, combined with the system's frequency stability requirements; its value is typically between -0.5 and 0. In the first constraint condition, the frequency-power relationship constraint adds an adjustment term related to the rate of change of photovoltaic power generation, based on the existing frequency-power relationship. This allows the control model to more accurately adjust the energy storage charging and discharging power based on the actual system state, further improving the frequency support effect. The first objective function can be solved using a model predictive control (MPC) algorithm under the first constraint condition. Based on the current system state parameters (including photovoltaic power generation, frequency, and fast electrochemical energy storage SOC, etc.) and predicted information parameters (including photovoltaic power generation prediction, etc.), the control strategy is continuously optimized. The fast electrochemical energy storage charging and discharging power at each moment within a short timescale is obtained and used as the output of the first charging and discharging control model.

[0071] S204. Construct a second charge-discharge control model based on the long-term output power prediction of the new energy power generation device and the power parameters of the power system. The long-term output power prediction can be obtained using a seasonal autoregressive integral moving average (SARIMA) model. The general form of the SARIMA model is:

[0072]

[0073] Among them, Y t This represents time-series data; in this example, it represents photovoltaic power generation from historical data. B represents the lag operator, p is the autoregressive order, d is the difference order, q is the moving average order, and s is the seasonal period. and θ j Represents model parameters, ∈ t This represents a white noise sequence. The model parameters p, q, d, and s can be selected based on the data characteristics and the model itself. The model parameters are obtained by training the model using historical data. and θ j This process yields a well-trained model. During training, methods such as maximum likelihood estimation can be used to solve for the model parameters, enabling the model to best fit historical data. For prediction, historical data is input into the model, and the model calculates the predicted long-term output power.

[0074] In this embodiment of the invention, different prediction methods are used to obtain short-term and long-term output power predictions for photovoltaic power generation, so that the photovoltaic power generation data used in the first and second charge-discharge control models conforms to the characteristics of different time scales. The prediction method provided in the above embodiments is an exemplary implementation method of the present invention. Based on the overall concept of the present invention, other feasible prediction methods can also be used to predict the power of long-term and short-term output power.

[0075] The second charge-discharge control model includes a second objective function and a second constraint condition. In this embodiment of the invention, the second objective function J2 is expressed as:

[0076]

[0077] Among them, C m-c and C m-d P represents the charging cost factor and discharging cost factor for slow physical energy storage devices, respectively. m,t Let t be the charging and discharging power of the slow physical energy storage device, with charging being negative and discharging being positive, and SOC being... m,op For slow physical energy storage devices, the optimal SOC (State of Charge) is... m,t Let P be the SOC value of the slow physical energy storage device at time t, δ be the power balance weighting coefficient, and γ be the influencing factor of the slow physical energy storage charge and discharge efficiency, which can be obtained by fitting historical data. pv,tl Let be the long-term power prediction value of photovoltaic power generation at time t. The second constraint conditions include the second power constraint condition, the second SOC constraint condition, the grid power balance constraint condition, and the charge / discharge rate constraint condition, which are expressed as follows:

[0078] -P m,max-c ≤P m,t ≤P m,max-d

[0079] SOC m-min ≤SOC m,t ≤SOC m-max

[0080] P load,t =P pv,tl +P m,t +P gin,t -P gout,t

[0081]

[0082] Among them, P m,max-c and P m,max-d These represent the maximum charging power and maximum discharging power of the slow physical energy storage device, respectively, and the State of Charge (SOC). m-min and SOCm-max These are the minimum and maximum SOC values ​​for a slow physical energy storage device, respectively. gin,t P is the power input from the power system. gout,t R is the power output to the power system. max This represents the upper limit of the charge / discharge rate for slow physical energy storage devices. Used to calculate the charge and discharge rates of slow physical energy storage devices.

[0083] The second objective function addresses both system power balance and the operating cost of slow physical energy storage. In the exponential terms of the first two terms, the charging and discharging costs of slow physical energy storage at different states of charge (SOC) are correlated with the deviation between the current SOC and the optimal SOC. When the SOC deviates from the optimal value, this cost increases, guiding the slow physical energy storage charging and discharging strategy towards bringing the SOC closer to the optimal value, thus enabling the energy storage device to operate stably and efficiently over the long term. The last term is the power balance term, and the weighting coefficient δ can be adjusted according to the actual system requirements to determine the relative importance of the power balance objective in the entire objective function, ensuring that slow physical energy storage plays a suitable role in maintaining power balance. The weighting coefficient δ can be set based on the actual operating conditions, the required power supply reliability, and the control of slow physical energy storage operating costs, while also referencing historical power fluctuation data. The first three constraints in the second set of conditions are standard constraints for normal system operation. The charging and discharging rate constraint is used to limit the potential damage to the equipment caused by excessively fast charging and discharging rates of slow physical energy storage. By limiting the charging and discharging rate, the long-term operating performance of the system can be further optimized. The second objective function can be solved under the second constraint by using the particle swarm optimization algorithm (PSO). Through particle encoding and iterative optimization, the iteration process ends when the preset termination condition is met (such as reaching the maximum number of iterations or the fitness value converging to a certain accuracy). At this time, the charging and discharging strategy corresponding to the global optimal position is the optimized slow physical energy storage charging and discharging strategy.

[0084] S206. The parameters of the second charge-discharge control model are corrected based on the output of the first charge-discharge control model to obtain the corrected second charge-discharge control model. In this embodiment of the invention, the power balance weight coefficient is corrected by the output of the first charge-discharge control model, enabling the second discharge control model to respond in real time to short-term output fluctuations, thereby improving the system's collaborative efficiency. This correction process specifically includes the following steps:

[0085] S2061. Solve the first charge-discharge control model under the first constraint to obtain the fast electrochemical energy storage charge-discharge power at each moment within a short time scale. The parameters of the second charge-discharge control model can be corrected according to a predetermined period and in conjunction with the output of the first charge-discharge control model. Calculate the average value of the fast electrochemical energy storage charge-discharge power output by the first charge-discharge control model within the predetermined period as the average charge-discharge power. Calculate the average value of the absolute value of the difference between the rated frequency and the actual frequency of the power system within the predetermined period as the average frequency deviation.

[0086] S2062. The second objective function is modified based on the average charge / discharge power and the average frequency deviation. The power balance weighting coefficient increases with the increase of the average frequency deviation, and can be expressed as:

[0087] δ=δ0+k1·Δf

[0088] Where δ0 represents the power balance weight coefficient before correction, δ represents the power balance weight coefficient after correction, k1 represents the first proportional coefficient, and Δf represents the average frequency deviation. When the average frequency deviation increases, it indicates that the system frequency fluctuates greatly. At this time, it is necessary to strengthen the importance of system power balance and encourage slow physical energy storage to participate more actively in power regulation. Therefore, it is necessary to increase the power balance weight coefficient according to the predetermined proportional coefficient. The first proportional coefficient can be obtained from historical data, actual operating conditions and experiments, and the value range is [0.1, 0.5].

[0089] When the average charge / discharge power is negative, the charging cost coefficient decreases as the average charge / discharge power decreases; when the average charge / discharge power is positive, the discharging cost coefficient decreases as the average charge / discharge power increases. This can be expressed as:

[0090]

[0091] Among them, C m-c0 C represents the uncorrected charging cost factor. m-c C represents the corrected charging cost coefficient. m-d0 C represents the discharge cost factor before correction. m-d This represents the corrected discharge cost coefficient. The average charge / discharge power is represented by k1, k2 represents the second proportionality coefficient (k2 > 0), and its maximum value is related to the range of the charging cost coefficient and the maximum charging power. The third proportionality coefficient (k3 < 0) represents the third proportionality coefficient (k3 < 0), and its minimum value is related to the range of the discharge cost coefficient and the maximum discharge power. When the average charge / discharge power is negative, it indicates excess photovoltaic power generation, and fast electrochemical energy storage is charging to absorb the excess energy. In this case, the charging cost coefficient is reduced and the discharge cost coefficient is increased according to the second proportionality coefficient to encourage slow physical energy storage to charge. When the average charge / discharge power is positive, it indicates a power deficit in the system, and fast electrochemical energy storage is discharging to support the load. In this case, slow physical energy storage needs to be encouraged to discharge to make up for the power gap. The third proportionality coefficient is used to increase the charging cost coefficient and decrease the discharge cost coefficient to encourage slow physical energy storage to discharge. The second and third proportionality coefficients can be obtained based on historical data, actual operating conditions, and experiments. While adjusting the cost coefficient, it is also necessary to dynamically relax or tighten the second constraint: increase the charge and discharge rate limit when a fast response is required, and relax the minimum and maximum SOC values ​​of slow physical energy storage devices as needed.

[0092] S208. A first charge-discharge control model is used to control the charge and discharge of a fast electrochemical energy storage device on a short timescale, and a modified second charge-discharge control model is used to control the charge and discharge of a slow physical energy storage device on a long timescale. On a short timescale, the output of the first charge-discharge control model is used to control the charge and discharge of the fast electrochemical energy storage device; the parameters of the second charge-discharge control model can be modified according to a predetermined period based on the output of the first charge-discharge control model, and on a long timescale, the output of the second charge-discharge control model is used to control the charge and discharge of the fast electrochemical energy storage device.

[0093] An embodiment of the present invention also provides a control device for multi-element hybrid energy storage coupled with new energy sources, comprising:

[0094] The first charge-discharge control model construction module is used to construct the first charge-discharge control model based on the short-time power prediction value of the new energy power generation device and the frequency parameters of the power system.

[0095] The second charge-discharge control model construction module is used to construct the second charge-discharge control model based on the long-term output power prediction value of the new energy power generation device and the power parameters of the power system.

[0096] The parameter correction module is used to correct the parameters of the second charge-discharge control model by combining the output of the first charge-discharge control model, so as to obtain the corrected second charge-discharge control model.

[0097] The charge / discharge control module is used to control the charge / discharge of a fast electrochemical energy storage device on a short timescale using a first charge / discharge control model, and to control the charge / discharge of a slow physical energy storage device on a long timescale using a modified second charge / discharge control model.

[0098] In the control device for multi-element hybrid energy storage coupled with new energy provided in the above embodiments of the present invention, the specific process by which each module realizes its function is the same as the steps of the control method for multi-element hybrid energy storage coupled with new energy provided in the above embodiments of the present invention, and its repeated description will be omitted here.

[0099] In summary, the embodiments of this invention relate to a control method and apparatus for multi-element hybrid energy storage coupled with new energy sources. The control method includes: constructing a first charge-discharge control model based on the short-term power prediction value of the new energy power generation device and the frequency parameters of the power system; constructing a second charge-discharge control model based on the long-term output power prediction value of the new energy power generation device and the power parameters of the power system; correcting the parameters of the second charge-discharge control model by combining the output of the first charge-discharge control model to obtain a corrected second charge-discharge control model; using the first charge-discharge control model to perform charge-discharge control on a fast electrochemical energy storage device on a short time scale, and using the corrected second charge-discharge control model to perform charge-discharge control on a slow physical energy storage device on a long time scale. The technical solution provided by the embodiments of this invention adopts a hierarchical control strategy, which solves the problem of fluctuation coordination of new energy power generation at different time scales. Through hybrid models and dynamic correction, it compensates for the control deviations that may be caused by inaccurate model predictions, while ensuring the consistency of the hierarchical control strategy, thereby providing an efficient and reliable energy storage control solution for new energy power generation systems.

[0100] It should be understood that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of this invention, technical features of the above embodiments or different embodiments can also be combined, steps can be implemented in any order, and many other variations exist regarding different aspects of one or more embodiments of the invention as described above, which are not provided in the details for the sake of brevity. The specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A control method for multi-element hybrid energy storage coupled with new energy sources, characterized in that, Including the following steps: The first charge-discharge control model is constructed based on the short-time power prediction value of the new energy power generation device and the frequency parameters of the power system. A second charge-discharge control model is constructed based on the long-term output power prediction of the new energy power generation device and the power parameters of the power system. The parameters of the second charge-discharge control model are corrected by combining the output of the first charge-discharge control model to obtain the corrected second charge-discharge control model. A first charge-discharge control model is used to control the charge and discharge of a fast electrochemical energy storage device on a short time scale, while a modified second charge-discharge control model is used to control the charge and discharge of a slow physical energy storage device on a long time scale.

2. The method according to claim 1, characterized in that, The first charge-discharge control model includes a first objective function and a first constraint condition; the first objective function J1 is expressed as: Among them, f ref f is the rated frequency of the power system. t Let P be the actual frequency of the power system at time t. e,t P represents the charge / discharge power of the fast electrochemical energy storage device at time t, where charging is negative and discharging is positive. pv,ts Let t be the short-time power prediction value of photovoltaic power generation at time t, and α be the power fluctuation impact factor of photovoltaic power generation, where α < 0; The first constraint includes a first power constraint, a first SOC constraint, and a frequency-power relationship constraint.

3. The method according to claim 2, characterized in that, The first power constraint condition is expressed as follows: -P e,max-c ≤P e,t ≤P e,max-d The first SOC constraint is expressed as follows: SOC min ≤SOC t ≤SOC max The frequency-power relationship constraint is expressed as follows: Among them, P e,max-c and P e,max-d These represent the maximum charging power and maximum discharging power of fast electrochemical energy storage, respectively, and the State of Charge (SOC). t Let SOC be the SOC value of the fast electrochemical energy storage device at time t. min and SOC max , respectively, represent the minimum and maximum SOC values ​​of the fast electrochemical energy storage device, D is the system frequency regulation coefficient, and β is the photovoltaic power generation regulation coefficient.

4. The method according to claim 2, characterized in that, The second charge-discharge control model includes a second objective function and a second constraint condition; the second objective function J2 is expressed as: Among them, C m-c and C m-d P represents the charging cost factor and discharging cost factor for slow physical energy storage devices, respectively. m,t Let t be the charging and discharging power of the slow physical energy storage device, with charging being negative and discharging being positive, and SOC being... m,op For slow physical energy storage devices, the optimal SOC (State of Charge) is... m,t Let δ be the SOC value of the slow physical energy storage device at time t, δ be the power balance weighting coefficient, γ be the influencing factor of the slow physical energy storage charge and discharge efficiency, and P be the SOC value of the device at time t. pv,tl Let t be the long-term power prediction value of photovoltaic power generation at time t; The second constraint includes the second power constraint, the second SOC constraint, the grid power balance constraint, and the charge / discharge rate constraint.

5. The method according to claim 4, characterized in that, The second power constraint is expressed as follows: -P m,max-c ≤P m,t ≤P m,max-d The second SOC constraint is expressed as follows: SOC m-min ≤SOC m,t ≤SOC m-max The power balance constraint condition of the power grid is expressed as follows: P load,t =P pv,tl +P m,t +P gin,t -P gout,t The charge / discharge rate constraint is expressed as follows: Among them, P m,max-c and P m,max-d These represent the maximum charging power and maximum discharging power of the slow physical energy storage device, respectively, and the State of Charge (SOC). m-min and SOC m-max These are the minimum and maximum SOC values ​​for a slow physical energy storage device, respectively. gin,t P is the power input from the power system. gout,t R is the power output to the power system. max This represents the upper limit of the charge / discharge rate for slow physical energy storage devices.

6. The method according to claim 4, characterized in that, The parameters of the second charge-discharge control model are corrected by combining the output of the first charge-discharge control model to obtain the corrected second charge-discharge control model, including the following steps: The parameters of the second charge-discharge control model are corrected according to the predetermined cycle and the output of the first charge-discharge control model. Under the first constraint conditions, the first charge-discharge control model is solved to obtain the fast electrochemical energy storage charge-discharge power. Calculate the average value of the fast electrochemical energy storage charging and discharging power output by the first charging and discharging control model within the predetermined period, as the average charging and discharging power; calculate the average value of the absolute value of the difference between the rated frequency and the actual frequency of the power system within the predetermined period, as the average frequency deviation. The second objective function is modified based on the average charge / discharge power and average frequency deviation.

7. The method according to claim 6, characterized in that, The correction of the second objective function based on the average charge / discharge power and average frequency deviation includes: The power balance weighting coefficients in the second objective function are corrected based on the average frequency deviation: δ=δ0+k1·Δf Where δ0 represents the power balance weighting coefficient before correction, δ represents the power balance weighting coefficient after correction, k1 represents the first proportional coefficient, and Δf represents the average frequency deviation.

8. The method according to claim 6, characterized in that, The correction of the second objective function based on the average charge / discharge power and average frequency deviation includes: The charging cost coefficient and discharging cost coefficient in the second objective function are corrected based on the average charging and discharging power: Among them, C m-c0 C represents the uncorrected charging cost factor. m-c C represents the corrected charging cost coefficient. m-d0 C represents the discharge cost factor before correction. m-d This represents the corrected discharge cost coefficient. k1 represents the average charge / discharge power, k2 represents the second proportional coefficient, and k3 represents the third proportional coefficient.

9. A control device for multi-element hybrid energy storage coupled with new energy sources, characterized in that, include: The first charge-discharge control model construction module is used to construct the first charge-discharge control model based on the short-time power prediction value of the new energy power generation device and the frequency parameters of the power system. The second charge-discharge control model construction module is used to construct the second charge-discharge control model based on the long-term output power prediction value of the new energy power generation device and the power parameters of the power system. The parameter correction module is used to correct the parameters of the second charge-discharge control model by combining the output of the first charge-discharge control model, so as to obtain the corrected second charge-discharge control model. The charge / discharge control module is used to control the charge / discharge of a fast electrochemical energy storage device on a short time scale using a first charge / discharge control model, and to control the charge / discharge of a slow physical energy storage device on a long time scale using a modified second charge / discharge control model.

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