New energy transformer control method considering hybrid energy storage access

Abstract

The invention belongs to the technical field of transformer control, and particularly relates to a new energy transformer control method considering hybrid energy storage access, and the method comprises the steps: building a new energy transformer grid-connected architecture which comprises a distributed power supply, an electrochemical energy storage and super-capacitor hybrid energy storage system and a load; establishing a new energy transformer node power matching model according to source network load storage input and output characteristics; a hybrid energy storage control model is established based on electrochemical energy storage and super capacitor charging and discharging characteristics; optimizing an input and output model of the hybrid energy storage system according to a time-of-use electricity price policy; constructing a multi-objective optimization function with the minimum fluctuation, the lowest loss and the maximum operation benefit; and carrying out normalization weighting on the minimum fluctuation, the lowest loss and the maximum operation income to solve a comprehensive optimization target, and dynamically adjusting the input power of the transformer. The method aims at optimizing cooperative work of an electrochemical energy storage system and a super-capacitor energy storage system, optimizing a new energy transformer adjusting strategy and achieving reliability and economics of system operation.

Description

Technical Field

[0001] The present invention belongs to the technical field of transformer control, and particularly relates to a control method for a new energy transformer considering the access of hybrid energy storage. Background Art

[0002] With the wide application of clean energy having become the core direction of future energy transformation. As a new type of power equipment, the new energy transformer has become an important part of the new energy power generation system because it can effectively integrate various distributed energy sources and energy storage devices. However, the power generation characteristics of the new energy system are often affected by natural conditions, such as the volatility and intermittency of wind energy and solar energy, resulting in some challenges for the new energy transformer in terms of power dispatching and grid stability.

[0003] To effectively solve this problem, connecting hybrid energy storage to the new energy transformer has become an effective technical means. Currently, single energy storage technologies such as electrochemical energy storage or supercapacitors have problems such as insufficient power density, slow energy response, and limited short-term energy storage capacity, resulting in poor response speed and effectiveness of a single energy storage device in dealing with sudden voltage fluctuations or power outages, which limits the potential of the new energy transformer in efficient and stable operation. Therefore, how to overcome these deficiencies and improve the dynamic regulation ability of the new energy transformer and the emergency response ability of the power grid has become a technical problem to be solved urgently.

[0004] To solve the above problems, the present invention proposes a control method for a new energy transformer considering the access of hybrid energy storage. This method combines the hybrid energy storage technology with the characteristics of the new energy transformer. By optimizing the energy dispatching of the energy storage system, it can more effectively cope with the volatility and intermittency problems in the new energy power generation system. By jointly controlling the hybrid energy storage system and the new energy transformer, the power matching accuracy of the new energy transformer is effectively improved, and the stability of the power grid and the power quality are also enhanced, providing a new solution for the efficient utilization of new energy and the construction of smart grids. Summary of the Invention

[0005] The purpose of the present invention is to provide a control method for a new energy transformer considering the access of hybrid energy storage, aiming to optimize the collaborative work of the electrochemical energy storage and the supercapacitor energy storage system, optimize the regulation strategy of the new energy transformer, and achieve the reliability and economy of system operation. Build an architecture for the new energy transformer with the access of power sources, grids, loads, and energy storage, fully consider the operating characteristics of the electrochemical energy storage and the supercapacitor energy storage system, and based on the time-of-use electricity price policy, with the goals of minimizing the fluctuations generated by new energy access, minimizing transformer losses, and maximizing operating benefits, control the regulation characteristics of the hybrid energy storage and the transformer, effectively improving the energy supply reliability and economy of the system.

[0006] The technical solutions adopted by the present invention are specifically as follows:

[0007] A control method for a new energy transformer considering the access of hybrid energy storage, including:

[0008] Build a grid-connected architecture for a new energy transformer that includes a distributed power source, an electrochemical energy storage and a supercapacitor hybrid energy storage system, and a load;

[0009] Establish a node power matching model for the new energy transformer according to the input and output characteristics of the source, grid, load, and energy storage;

[0010] Based on the charge and discharge characteristics of the electrochemical energy storage and the supercapacitor, establish a hybrid energy storage control model;

[0011] Optimize the input and output model of the hybrid energy storage system according to the time-of-use electricity price policy;

[0012] Construct a multi-objective optimization function with minimum fluctuation, minimum loss, and maximum operating income;

[0013] Normalize and weighted-solve the comprehensive optimization objectives of minimum fluctuation, minimum loss, and maximum operating income, and dynamically adjust the input power of the transformer.

[0014] In a preferred solution, the node power matching model of the new energy transformer is:

[0015] P W (t)+P PV (t)+P ESS (t)=P Load (t)+P G (t)+P Loss (t)(1)

[0016] Where: P W (t) is the output characteristic of the wind turbine, P PV (t) is the output characteristic of the photovoltaic system, P ESS (t) is the input and output characteristic of the hybrid energy storage system, P Load (t) is the load demand power, P[[ID=I48]] G (t) is the grid output power, P Loss (t) is the power loss model of the new energy transformer.

[0017] In a preferred solution, the hybrid energy storage control model is:

[0018]

[0019] In the formula: P ES (t) is the input and output characteristic of the electrochemical energy storage system, P SC (t) is the input and output characteristic of the supercapacitor, α is the power distribution adjustment coefficient, ΔP G (t) is the grid output power deviation.

[0020] In a preferred embodiment, according to the time-of-use electricity price policy, the input-output model of the hybrid energy storage system is optimized as follows:

[0021]

[0022] In the formula: i = 1, 2, 3, representing the peak period, normal period, and off-peak period respectively, T i is the corresponding time, η cha , η dis are the charging and discharging efficiencies respectively, P ESS_cha (t), P ESS_dis (t) are the charging and discharging powers of the hybrid energy storage system, C P (t) is the time-of-use electricity price.

[0023] In a preferred embodiment, the multi-objective optimization function is:

[0024]

[0025] In the formula: P avg (t) is the smooth power output of the distributed power source after being regulated by the hybrid energy storage and the transformer, P in (t) is the transformer input power, η(t) is the transformer conversion efficiency, ΔP Load (t) is the grid output power deviation.

[0026] In a preferred embodiment, the steps of normalizing and weighting the minimum fluctuation, minimum loss, and maximum operating income to solve the comprehensive optimization objective and dynamically adjusting the transformer input power include:

[0027] Normalize the minimum fluctuation f1(t), minimum transformer loss f2(t), and maximum operating income f3(t) to obtain f1’(t), f2’(t), f3’(t) as follows:

[0028]

[0029] In the formula: minf1, maxf1, minf2, maxf2, minf3, maxf3 are the minimum and maximum extreme values obtained from the three objective functions;

[0030] The overall optimization objective is obtained as:

[0031] f(t) = max[w2f2′(t) - w1f1′(t) - w3f3′(t)] (6)

[0032] In the formula: w1, w2, w3 are the weight coefficients, w 1+ w2 + w3 = 1.

[0033] Optimize the objective according to formula (5), set the weight coefficients w1, w2, and w3 according to the proportion of the objective requirements, solve the optimization objective, adjust the control strategies of the hybrid energy storage and the new energy transformer, and obtain the power P for real-time regulation of the transformer in (t).

[0034] In a preferred embodiment, the step of dynamically regulating the input power of the transformer includes:

[0035] Calculate the optimized objective value f(t) at the current time period according to formula (6), and generate the charge and discharge instructions of the hybrid energy storage system and the transformer power regulation instructions;

[0036] Control the power distribution ratio between the electrochemical energy storage and the supercapacitor by adjusting the power distribution adjustment coefficient α;

[0037] Take the optimized P in (t) as the transformer input power set value, and realize dynamic power tracking through the converter.

[0038] In a preferred embodiment, the adjustment of the power distribution adjustment coefficient α includes:

[0039] When |ΔPG(t)| > 10% P rated (t), set α = 0.4 and call the supercapacitor for fast power compensation, where P rated (t) is the rated power capacity of the new energy transformer grid-connected framework;

[0040] When ΔPG(t) > 0 continuously appears for 0.5 h, distribute it to the electrochemical energy storage for energy-type regulation according to α = 0.2;

[0041] Dynamically correct in combination with the SOC state of the supercapacitor: when SOCSC < 30%, α = α × 0.7.

[0042] In a preferred embodiment, the establishment of the power loss model P Loss (t) includes:

[0043] Take into account the non-linear characteristics of the iron loss and copper loss of the transformer: PLoss(t) = k1P2in(t) + k2U 2 (t)

[0044] where k1 is the load loss coefficient, k2 is the no-load loss coefficient, and U(t) is the real-time terminal voltage;

[0045] Introduce a temperature correction factor: k2' = k2[1 + 0.015(T(t) - 25)], where T(t) is the real-time temperature of the winding.

[0046] In a preferred embodiment, the method for determining the weight coefficients w1, w2, and w3 includes:

[0047] Set w3 > w1 > w2 during peak hours to preferentially ensure operating revenue;

[0048] Set w1 > w2 > w3 during weak grid periods to strengthen fluctuation suppression;

[0049] Dynamically increase the weight of w2 according to the transformer aging index, and set w2 = 0.6 when the insulation loss rate exceeds the threshold.

[0050] The technical effects achieved by the present invention are as follows: In the present invention, by constructing a new energy transformer grid-connected architecture including hybrid energy storage, a coordinated control mechanism is established based on the complementary characteristics of electrochemical energy storage and supercapacitors to perform peak shaving and frequency modulation on a large-scale new energy-connected power system. Considering the volatility of large-scale new energy access, the operation of the new energy transformer is optimized by adjusting the characteristics of the hybrid energy storage system, improving the energy conversion efficiency and power quality, and enhancing the operation reliability and economic benefits of the transformer.

[0051] The supercapacitor of the present invention quickly suppresses the second-level power fluctuations of wind power and photovoltaic power relying on the millisecond-level response characteristics, while the electrochemical energy storage eliminates the hourly energy deviation through the SOC coordination strategy. The two cooperate to reduce the volatility of new energy output. At the same time, based on the time-of-use electricity price policy, combined with the multi-objective optimization function, the input power is adjusted in real time through the dynamic weight coefficient. Description of the Drawings

[0052] Figure 1 is the system block diagram of the embodiment of the present invention;

[0053] Figure 2 is the control flow chart of the new energy transformer with hybrid energy storage access of the present invention;

[0054] Figure 3 is the topological structure diagram of the new energy transformer with hybrid energy storage access of the present invention. Detailed Embodiments

[0055] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given in conjunction with the drawings of the specification.

[0056] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0057] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0058] Embodiment 1

[0059] Please refer to the attached drawings to Figure 3 As shown, this is the first embodiment of the present invention, which provides a control method for a new energy transformer considering the access of hybrid energy storage, including:

[0060] Build a grid-connected architecture for a new energy transformer that includes a distributed power source, an electrochemical energy storage and a supercapacitor hybrid energy storage system, and a load. Among them, the load is connected to the grid through the grid-connected architecture of the new energy transformer, integrating distributed power sources such as photovoltaic and wind power, combining electrochemical energy storage and supercapacitors to form a hybrid energy storage system, and realizing grid-connected operation through the new energy transformer. The specific architecture is as Figure 3 shown;

[0061] According to the input-output characteristics of the source-grid-load-storage, establish a node power matching model for the new energy transformer. Among them, the node power matching model for the new energy transformer is:

[0062] P W (t) + P PV (t) + P ESS (t) = P Load (t) + P G (t) + P Loss (t)(1)

[0063] Among them: P W (t) is the output characteristic of the wind turbine; P PV (t) is the output characteristic of the photovoltaic system; P ESS (t) is the input-output characteristic of the hybrid energy storage system; P Load (t) is the load demand power; P G (t) is the grid output power; P Loss (t) is the power loss model of the new energy transformer;

[0064] Based on the charge and discharge characteristics of the electrochemical energy storage and the supercapacitor, establish a hybrid energy storage control model. Among them, the hybrid energy storage control model is:

[0065]

[0066] In the formula: P ES (t) is the input-output characteristic of the electrochemical energy storage system; P SC(t) is the input and output characteristics of the supercapacitor; α is the power distribution adjustment coefficient; ΔP G (t) is the power deviation of the power grid output;

[0067] According to the time-of-use electricity price policy, optimize the input and output model of the hybrid energy storage system. Specifically, optimizing the input and output model of the hybrid energy storage system is as follows:

[0068]

[0069] In the formula: i = 1, 2, 3, representing the peak period, normal period, and off-peak period respectively, and T i is the corresponding time; η cha , η dis are the charging and discharging efficiencies respectively, P ESS_cha (t), P ESS_dis (t) are the charging and discharging powers of the hybrid energy storage system, C P (t) is the time-of-use electricity price;

[0070] Construct a multi-objective optimization function with minimum fluctuation, minimum loss, and maximum operating income. Among them, the multi-objective optimization function is:

[0071]

[0072] In the formula: P avg (t) is the smooth power output of the distributed power source adjusted by the hybrid energy storage and the transformer, P in (t) is the input power of the transformer, η(t) is the transformer conversion efficiency, and ΔP Load (t) is the power deviation of the power grid output;

[0073] Perform normalized weighted solution for the comprehensive optimization objective of minimum fluctuation, minimum loss, and maximum operating income, and dynamically adjust the input power of the transformer, specifically including:

[0074] Normalize the minimum fluctuation f1(t), minimum transformer loss f2(t), and maximum operating income f3(t) to obtain f1’(t), f2’(t), and f3’(t) as follows:

[0075]

[0076] In the formula: minf1, maxf1, minf2, maxf2, minf3, maxf3 are the minimum and maximum extreme values obtained from the three objective functions;

[0077] The overall optimization objective is obtained as:

[0078] f(t) = max[w2f2′(t) - w1f1′(t) - w3f3′(t)](6)

[0079] where: w1, w2, w3 are weight coefficients, w 1+ w2 + w3 = 1.

[0080] According to the optimization objective of formula (5), the weight coefficients w1, w2, w3 are set according to the proportion of the target requirements, the optimization objective is solved, the control strategies of the hybrid energy storage and the new energy transformer are adjusted, and the real-time regulated power P of the transformer is obtained. in (t).

[0081] In the above embodiment, by constructing a new energy transformer grid-connected architecture including hybrid energy storage, a coordinated control mechanism is established based on the complementary characteristics of electrochemical energy storage and supercapacitors to perform peak shaving and frequency modulation on a large-scale new energy connected to the power system. Considering the volatility of large-scale new energy access, the operation of the new energy transformer is optimized by adjusting the characteristics of the hybrid energy storage system, the energy conversion efficiency and power quality are improved, and the operation reliability and economic benefits of the transformer are enhanced.

[0082] In a preferred embodiment, the steps of dynamically regulating the input power of the transformer include:

[0083] Calculate the optimized target value f(t) of the current period according to formula (6), and generate the charge and discharge instructions of the hybrid energy storage system and the transformer power regulation instructions;

[0084] By adjusting the power distribution adjustment coefficient α, control the power distribution ratio between the electrochemical energy storage and the supercapacitor;

[0085] Take the optimized P in (t) as the transformer input power set value, and realize power dynamic tracking through the converter.

[0086] During the actual regulation process, the grid output power deviation ΔP G (t), the time-of-use electricity price C P (t) and the load demand fluctuation ΔP Load (t) are obtained in real time; calculate the comprehensive optimized target value f(t) of the current period according to formula (6), and generate the charge and discharge instructions of the hybrid energy storage and the transformer power regulation instructions; then by adjusting the power distribution adjustment coefficient α, control the power distribution ratio between the electrochemical energy storage and the supercapacitor, and take the optimized P in (t) as the transformer input power set value, and realize power dynamic tracking through the converter. Above, through the closed-loop control logic of real-time perception - dynamic optimization - precise execution, the fast response characteristics of the hybrid energy storage are deeply combined with the loss optimization of the transformer.

[0087] On the basis of the above embodiment, specifically adjusting the power distribution adjustment coefficient α is:

[0088] When |ΔPG(t)| > 10%P rated (t), set α = 0.4 and call the supercapacitor for rapid power compensation. P rated (t) is the rated power capacity of the new energy transformer grid-connected architecture;

[0089] When ΔPG(t) > 0 continuously appears for 0.5 h, allocate it to the electrochemical energy storage for energy regulation according to α = 0.3;

[0090] Dynamically correct it in combination with the supercapacitor SOC state: when SOC SC < 30%, α = α × 0.7.

[0091] In the above steps, the supercapacitor is called for high-frequency fluctuations: when the grid power deviation |ΔPG(t)| > 10%P rated (t) (such as sudden load addition or sudden drop in new energy output), set α = 0.4, that is, the supercapacitor undertakes 40% of the power compensation task, and the millisecond-level response (<100 ms) of the supercapacitor can quickly suppress the fluctuations and avoid grid transient instability. For medium- and long-term regulation, the electrochemical energy storage is called. For example, when the power deviation lasts for more than 0.5 h (such as the decrease in photovoltaic output at noon), set α = 0.2, and the electrochemical energy storage undertakes 80% of the regulation task. The high energy density characteristics of lithium batteries / flow batteries are suitable for handling long-term energy gaps. Finally, dynamically correct it in combination with SOC. When the charge state (SOC SC ) of the supercapacitor is lower than 30%, the α value is reduced proportionally (such as α = 0.4 × 0.7 = 0.28) to avoid over-discharging and damaging the capacitor life.

[0092] As above, the response time of the supercapacitor is improved from the second level (>1 s) of traditional energy storage to the millisecond level (<100 ms), and the short-term fluctuation suppression rate is increased. Example: when the wind power output suddenly drops by 2 MW (rated power 10 MW), the supercapacitor compensates 1.6 MW within 50 ms, and the remaining 0.4 MW is processed by the electrochemical energy storage. In addition, through the α correction associated with SOC, the cycle times of the supercapacitor are increased. The deep discharge times of the electrochemical energy storage are reduced, and the service life is extended.

[0093] Specifically, during the valley period of electricity price, force the electrochemical energy storage to charge to SOC ES ≥90%. When it is predicted that the wind power output will decrease the next day, keep the supercapacitor SOC SC ≥40% as emergency backup, and trigger the SOC balance mode during grid faults: P ES (t) = 0.7(SOC ES - 50%)P ES_ratedFor example, during the low electricity price period (such as 0:00-6:00 in the morning, when electricity prices are lowest), electrochemical energy storage (such as lithium batteries) can be charged at the lowest cost to ensure that its state of charge reaches the best state.

[0094] In the above process, the power loss model P Loss (t) The establishment includes:

[0095] Taking into account the nonlinear characteristics of transformer iron loss and copper loss: P Loss (t) = k1P 2 in (t)+k2U 2 (t)

[0096] Where k1 is the load loss coefficient, k2 is the no-load loss coefficient, and U(t) is the real-time terminal voltage;

[0097] A temperature correction factor is introduced: k2'=k2[1+0.015(T(t)-25)], where T(t) is the real-time winding temperature and k2' is the corrected no-load loss coefficient. The traditional model assumes that the no-load loss coefficient is constant by default, but in actual operation, the transformer winding temperature will change with the load and environment. By introducing k2', the model can dynamically reflect the impact of temperature on iron loss and improve the accuracy of loss calculation.

[0098] Among them, copper loss (load loss) is proportional to the square of input power, and iron loss (no-load loss) is proportional to the square of terminal voltage. The introduction of winding temperature correction factor improves the accuracy of iron loss estimation and avoids the risk of transformer overheating. With the adjustment of adjustment coefficient α, the time integral of copper loss is reduced and power loss is more accurate. At the same time, P is collected in real time by sensor. in (t), U(t) and winding temperature T(t), dynamically update the loss model and provide accurate input for the multi-objective optimization function.

[0099] In addition, when temperature correction is introduced, a joint optimization of temperature and power can also be established. During high temperature periods (such as T>70°C), the α value adjustment is automatically triggered, and supercapacitors are preferentially called (to reduce the transformer load caused by the charging and discharging of electrochemical energy storage). At the same time, the input power is limited through the loss model to prevent the temperature from exceeding the standard.

[0100] In another preferred embodiment, the method for determining the weight coefficients w1, w2, and w3 includes:

[0101] During peak hours, w3>w1>w2 is set to prioritize operational benefits;

[0102] During the vulnerable period of the power grid, w1>w2>w3 is set to enhance fluctuation suppression;

[0103] Dynamically increase the weight w2 according to the transformer aging index, and set w2 = 0.6 when the insulation loss rate exceeds the threshold.

[0104] During peak hours, since the electricity price is the highest, economy can be given priority to maximize the energy storage discharge benefit. When the power grid is vulnerable (volatility is greater than the corresponding threshold or frequency deviation is greater than the corresponding threshold), fluctuation suppression is the main focus. That is, when a power grid stability risk is detected, the supercapacitor is forced to quickly compensate for the power gap, and the weight ratio of fluctuation suppression is 60%. At the same time, an aging index A(t) (range 0 - 1, where 1 represents severe aging) is generated based on the transformer insulation loss rate (such as the degree of oil-paper aging) and the cumulative operation time. When the aging index is greater than the threshold, by restricting P in (t) not to exceed a fixed ratio (such as 80%) of the rated value to avoid accelerating aging.

[0105] In summary, in the present invention, by constructing a new energy transformer grid-connected architecture including hybrid energy storage, a cooperative control mechanism is established based on the complementary characteristics of electrochemical energy storage and supercapacitors to perform peak shaving and frequency modulation for a large-scale new energy connected to the power system. Considering the volatility of large-scale new energy access, the operation of the new energy transformer is optimized by adjusting the characteristics of the hybrid energy storage system to improve the energy conversion efficiency and power quality, and enhance the operation reliability and economic benefits of the transformer. The supercapacitor relies on the millisecond-level response characteristic to quickly suppress the second-level power fluctuations of wind power and photovoltaic power, and the electrochemical energy storage eliminates the hourly energy deviation through the SOC coordination strategy. The two cooperate to reduce the volatility of new energy output. At the same time, based on the time-of-use electricity price policy, combined with a multi-objective optimization function, the input power is adjusted in real time through dynamic weight coefficients.

[0106] The above is only the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention are implemented according to the conventional means in this field without special description and limitation.

Claims

1. A control method for a new energy transformer considering the access of hybrid energy storage, characterized in that Including: Build a grid-connected architecture of a new energy transformer that includes a distributed power source, a hybrid energy storage system of electrochemical energy storage and supercapacitors, and a load; Establish a node power matching model for the new energy transformer according to the input and output characteristics of the source, grid, load, and energy storage; Based on the charge and discharge characteristics of electrochemical energy storage and supercapacitors, establish a hybrid energy storage control model; Optimize the input and output model of the hybrid energy storage system according to the time-of-use electricity price policy; Construct a multi-objective optimization function with minimum fluctuation, minimum loss, and maximum operating income; Normalize and weighted solve the comprehensive optimization objectives of minimum fluctuation, minimum loss, and maximum operating income, and dynamically adjust the input power of the transformer.

2. The new energy transformer control method considering the access of hybrid energy storage according to claim 1, characterized in that: The node power matching model of the new energy transformer is: P W (t) + P PV (t) + P ESS (t) = P Load (t) + P G (t) + P Loss (t) (1) Among them: P W (t) is the output characteristic of the wind turbine, P PV (t) is the output characteristic of the photovoltaic system, P ESS (t) is the input-output characteristic of the hybrid energy storage system, P Load (t) is the load demand power, P G (t) is the grid output power, P Loss (t) is the power loss model of the new energy transformer.

3. The new energy transformer control method considering the access of hybrid energy storage according to claim 1, characterized in that: The hybrid energy storage control model is: Where: P ES (t) is the input-output characteristic of the electrochemical energy storage system, P SC (t) is the input-output characteristic of the super capacitor, α is the power distribution adjustment coefficient, ΔP G (t) is the power output deviation of the power grid.

4. The new energy transformer control method considering the access of hybrid energy storage according to claim 1, characterized in that: The optimization of the input and output model of the hybrid energy storage system according to the time-of-use electricity price policy is specifically: Where: i = 1, 2, 3, representing the peak period, normal period, and off-peak period respectively, and T i is the corresponding time, η cha , η dis are the charging and discharging efficiencies respectively, P ESS_cha (t), P ESS_dis (t) are the charging and discharging powers of the hybrid energy storage system, and C P (t) is the time-of-use electricity price.

5. The new energy transformer control method considering the access of hybrid energy storage according to claim 1, characterized in that: The multi-objective optimization function is: Where: P avg (t) is the smooth power output of the distributed power source after being regulated by the hybrid energy storage and the transformer, P in (t) is the transformer input power, η(t) is the transformer conversion efficiency, ΔP Load (t) is the grid output power deviation.

6. The new energy transformer control method considering the access of hybrid energy storage according to claim 1, characterized in that: The steps of normalizing and weighted solving the comprehensive optimization objectives of minimum fluctuation, minimum loss, and maximum operating income, and dynamically adjusting the input power of the transformer include: Normalize the minimum fluctuation f1(t), minimum transformer loss f2(t), and maximum operating income f3(t) to obtain f1’(t), f2’(t), f3’(t) as: In the formula: minf1, maxf1, minf2, maxf2, minf3, maxf3 are the minimum and maximum extreme values obtained from the three objective functions; The overall optimization objective is obtained as: f(t) = max[w2f2′(t) - w1f1′(t) - w3f3′(t)] (6) where: w1, w2, w3 are weight coefficients, w 1+ w2 + w3 = 1. Optimize the objective according to formula (5), set the weight coefficients w1, w2, w3 according to the proportion of the target requirements, solve the optimization objective, adjust the control strategies of the hybrid energy storage and the new energy transformer, and obtain the real-time regulated power P in (t).

7. The new energy transformer control method considering the access of hybrid energy storage according to claim 1, characterized in that: The steps of dynamically adjusting the input power of the transformer include: Calculate the optimization objective value f(t) at the current time according to formula (6), and generate charge and discharge commands for the hybrid energy storage system and transformer power adjustment commands; Control the power distribution ratio of electrochemical energy storage and supercapacitors by adjusting the power distribution adjustment coefficient α; Take the optimized P in (t) as the set value of the transformer input power, and realize the dynamic power tracking through the converter.

8. The new energy transformer control method considering the access of hybrid energy storage according to claim 7, characterized in that: The adjustment of the power distribution adjustment coefficient α includes: When |ΔPG(t)| > 10%P rated (t), set α = 0.4 and call the supercapacitor for fast power compensation. P rated (t) is the rated power capacity of the new energy transformer grid-connected architecture; When ΔPG(t) > 0 continuously appears for 0.5h, allocate it to the electrochemical energy storage for energy-type regulation according to α = 0.2; Dynamically correct in combination with the SOC state of the supercapacitor: when SOCSC < 30%, α = α × 0.

7.

9. The new energy transformer control method considering the access of hybrid energy storage according to claim 1, wherein: The power loss model P Loss (t) is established as follows: Taking into account the non-linear characteristics of transformer iron loss and copper loss: PLoss(t) = k1P2in(t) + k2U 2 (t) Where k1 is the load loss coefficient, k2 is the no-load loss coefficient, and U(t) is the real-time terminal voltage; Introduce a temperature correction factor: k2' = k2[1 + 0.015(T(t) - 25)], where T(t) is the real-time temperature of the winding.

10. The new energy transformer control method considering the access of hybrid energy storage according to claim 1, characterized in that: The determination method of the weight coefficients w1, w2, w3 includes: Set w3 > w1 > w2 during peak hours to give priority to ensuring operating income; Set w1 > w2 > w3 during grid vulnerable periods to strengthen fluctuation suppression; Dynamically increase the weight of w2 according to the transformer aging index, and set w2 = 0.6 when the insulation loss rate exceeds the threshold.

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