A photovoltaic power prediction and control method based on energy storage system

Abstract

The present invention relates to a photovoltaic power prediction and control method based on an energy storage system. In the joint operation mode of a photovoltaic power station and an energy storage system, the method takes into account the error assessment of photovoltaic short-term power prediction and ultra-short-term power prediction in the power auxiliary service market while controlling the energy storage power output, and takes into account the state of the energy storage system in real time, and tries to balance the energy storage SOC state in a better state while satisfying the power prediction assessment. Compared with the prior art, the present invention has the advantages of solving the problem of taking into account the error control of short-term power prediction and ultra-short-term power prediction in the application control of photovoltaic stations, and solving the problem of large calculation amount and poor adaptability of traditional control strategies in engineering applications.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic and energy storage control, and in particular to a photovoltaic power prediction and control method based on an energy storage system. Background Art

[0002] Under the dual carbon goals, photovoltaic power generation has developed rapidly across the country in recent years because it meets the conditions of clean, efficient and renewable. However, photovoltaic power generation is easily affected by the external environment, and there are problems such as unstable output and large volatility. The grid absorption capacity and power system regulation resources that are compatible with its development scale are gradually facing challenges. The introduction of energy storage technology can achieve energy transfer, reduce the power fluctuation range, and smooth power output. The joint operation mode of photovoltaic storage has become an effective means to solve this series of problems. At present, photovoltaic sites are based on the coordinated control of energy storage systems, and reducing power prediction errors and reducing photovoltaic sites. Power prediction deviation assessment has become an important research and application scenario. On the one hand, the current research on photovoltaic storage coordinated control has considered factors such as power prediction deviation, energy storage SOC state, and energy storage life management as control targets and constraints, but in the control solution, more intelligent algorithm optimization and polynomial analytical optimization methods are used, and the adaptability to engineering applications is poor. On the other hand, when the power prediction error is used as the control target, only one of the short-term power prediction or ultra-short-term power prediction is often considered. In view of the current situation in which the photovoltaic power forecasting auxiliary service market has errors in both short-term power forecasting and ultra-short-term power forecasting, photovoltaic storage related control strategies need further research and application.

[0003] After searching, Chinese patent publication number CN114298441A discloses a photovoltaic power prediction method and system, which specifically discloses that firstly, a correlation analysis is performed on the influencing factor set, and the influencing factors with correlation greater than the set value in the influencing factor set are selected to obtain a related factor set; and the historical data of the related factor set is obtained to obtain a related data set; then the related data set is preprocessed to obtain a preprocessed data set; and the prediction model is trained based on the preprocessed data set to obtain the trained prediction model; finally, based on the trained prediction model, combined with the prediction data of each of the related factors, the photovoltaic power is predicted. However, the existing patent does not consider the impact of the energy storage system, so how to solve the problem of the energy storage system taking into account both short-term power prediction and ultra-short-term power prediction error control in the control of photovoltaic field station applications, and how to solve the problem of large computational complexity and poor adaptability of traditional control strategies in engineering applications, has become a technical problem that needs to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a photovoltaic power prediction and control method based on an energy storage system in order to overcome the defects of the above-mentioned prior art.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] According to one aspect of the present invention, a photovoltaic power prediction and control method based on an energy storage system is provided. In the joint operation mode of the photovoltaic power station and the energy storage system, the method takes into account the error assessment of photovoltaic short-term power prediction and ultra-short-term power prediction in the power auxiliary service market while controlling the energy storage power output, and takes into account the state of the energy storage system in real time, so as to balance the energy storage SOC state in a better state while satisfying the power prediction assessment.

[0007] As a preferred technical solution, the method specifically comprises the following steps:

[0008] Step 1) Obtain the short-term power prediction value P corresponding to the current moment from the photovoltaic power prediction system fd and ultra-short-term power prediction value P fc ; Obtain the maximum discharge power P1 and maximum charging power P2 currently allowed by the energy storage system; Obtain the power generation power P of the photovoltaic station;

[0009] Step 2) Determine the adjustment range A allowed for short-term power forecasting: [P fd,min P fd,max ], determine the adjustment range B allowed for ultra-short-term power forecasting as: [P fc,min P fc,max ], the power regulation range C of photovoltaic and energy storage systems is determined as: [P min P max ];

[0010] Step 3) Determine the adjustment range of the energy storage system [P a P b ];

[0011] Step 4) Determine the energy storage system power command P according to the energy storage system power adjustment range and the energy storage system SOC state MW , while meeting the power prediction assessment, try to balance the energy storage SOC state to a better state.

[0012] As a preferred technical solution, the boundary value of the adjustment range in step 2 is specifically calculated as follows:

[0013]

[0014] Where: ΔP d ΔP is the allowable control deviation for short-term power prediction; c Allowable control deviation for ultra-short-term power prediction.

[0015] As a preferred technical solution, the step 3 is specifically as follows:

[0016] 301) If satisfied Then P a =P b =0;

[0017] 302) If satisfied

[0018] but,

[0019] 303) If satisfied

[0020] but,

[0021] 304) If satisfied

[0022] but

[0023] As a preferred technical solution, the Specifically: if the photovoltaic energy storage system cannot control the short-term and ultra-short-term power forecast errors within the allowable range, the energy storage system will not participate in power regulation.

[0024] As a preferred technical solution, the Specifically, the photovoltaic storage system can only control the ultra-short-term power prediction error within the allowable range, so the energy storage system participates in the ultra-short-term power prediction error control.

[0025] As a preferred technical solution, the Specifically: the photovoltaic storage system can control the short-term power forecast error within the allowable range, but the adjustment range of short-term power forecast and ultra-short-term power forecast do not overlap. The photovoltaic storage system cannot meet both requirements at the same time, so priority is given to short-term power forecast error control, which has a higher assessment cost.

[0026] As a preferred technical solution, the Specifically: if the photovoltaic storage system can simultaneously control the short-term and ultra-short-term power prediction errors within the allowable range, the energy storage system will coordinate the control of the short-term and ultra-short-term power prediction errors.

[0027] As a preferred technical solution, the step 4 is specifically as follows:

[0028] 401) If the energy storage system is charged, that is, P b ≤0, then

[0029] 402) If the energy storage system discharges, that is, P a ≥0, then

[0030] 403) If the energy storage system can be discharged and charged, that is, P a <0 and P b >0, then

[0031]

[0032] SOC is the state of charge of the energy storage system.

[0033] As a preferred technical solution, when the short-term power forecast, ultra-short-term power forecast and actual power of the photovoltaic system at night are all less than ΔP c When the power regulation lower limit of the photovoltaic storage system is: P min =max(P+P2,0), reducing the amount of electricity offline at the photovoltaic station without affecting the power forecast assessment.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] 1) The present invention solves the problem that the energy storage system takes into account both short-term power prediction and ultra-short-term power prediction error control in the application control of photovoltaic stations, and solves the problem that the traditional control strategy has a large amount of calculation and poor adaptability to engineering applications;

[0036] 2) In the joint operation mode of the photovoltaic power station and the energy storage system, the energy storage power output control of the present invention takes into account the error assessment of the photovoltaic short-term power forecast and the ultra-short-term power forecast in the power auxiliary service market, and takes into account the state of the energy storage system in real time, while meeting the power forecast assessment and balancing the energy storage SOC state to a better state, which is beneficial to the energy storage life management and makes the energy storage system have a large margin for charging and discharging. At the same time, a simple control decision method is designed to reduce the amount of calculation and adapt to practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0039] like Figure 1 As shown in the figure, a 100MW photovoltaic station is equipped with a 5MW / 10MWh battery energy storage capacity to achieve photovoltaic power prediction and coordinated control based on energy storage:

[0040] 1) Obtain the short-term power prediction value P corresponding to the current moment from the photovoltaic power prediction system fd and ultra-short-term power prediction value P fc ; Obtain the maximum discharge power P1 (discharge is positive) and maximum charging power P2 (charge is negative) currently allowed by the energy storage system;

[0041] 2) Determine the allowable adjustment range A of short-term power forecast as: [P fd,min P fd,max ], determine the adjustment range B allowed for ultra-short-term power forecasting as: [P fc,min P fc,max ], the power regulation range C of photovoltaic and energy storage systems is determined as: [P min P max ].

[0042]

[0043] Where P is the power generated by the photovoltaic station; ΔP d is the control deviation allowed for short-term power forecast, 10MW; ΔP c The allowable control deviation for ultra-short-term power prediction is 3MW.

[0044] 3) Determine the regulation range of the energy storage system [P a P b ]:

[0045] a) (If the PV-storage system cannot control the short-term and ultra-short-term power forecast errors within the allowable range, the energy storage system will not participate in power regulation)

[0046] then P a =P b =0

[0047] b) (The PV storage system can only control the ultra-short-term power prediction error within the allowable range, so the energy storage system participates in the ultra-short-term power prediction error control)

[0048] then P a =max(P fc,min ,P min )-P

[0049] P b =min(P fc,max ,P max )-P

[0050] c) (The photovoltaic storage system can control the short-term power prediction error within the allowable range, but the adjustment range of short-term power prediction and ultra-short-term power prediction do not overlap, and the photovoltaic storage system cannot meet the requirements of both at the same time. Priority is given to the control of short-term power prediction error, which has a higher assessment cost)

[0051] then P a =max(P fd,min ,P min )-P

[0052] P b =min(P fd,max ,P max )-P

[0053] d) (The PV storage system can simultaneously control the short-term and ultra-short-term power prediction errors within the allowable range, and the energy storage system coordinates the short-term and ultra-short-term power prediction error control)

[0054] then P a =max(P fd,min ,P fc,min ,P min )-P

[0055] P b =min(P fd,max ,P fc,max ,P max )-P

[0056] 4) Determine the energy storage system power command P according to the energy storage system power adjustment range and the energy storage system SOC state MW , while meeting the power forecast assessment, try to balance the energy storage SOC state in the middle position, which is beneficial to the energy storage life management and allows the energy storage system to have a large margin for charging and discharging.

[0057] a)if(P b ≤0)(Energy storage system charging)

[0058]

[0059] b)if(P a ≥0)(Energy storage system discharge)

[0060]

[0061] c)if(P a <0andP b >0)(Energy storage system can discharge and charge)

[0062]

[0063] When the short-term power forecast, ultra-short-term power forecast and actual power of the photovoltaic system at night are all less than ΔP c When the power regulation lower limit of the photovoltaic storage system is: P min =max(P+P2,0), reducing the amount of electricity offline at the photovoltaic station without affecting the power forecast assessment.

[0064] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A photovoltaic power prediction and control method based on an energy storage system, characterized in that: In the joint operation mode of photovoltaic power station and energy storage system, this method takes into account the error assessment of photovoltaic short-term power forecast and ultra-short-term power forecast in the power auxiliary service market while controlling the energy storage power output, and takes into account the state of the energy storage system in real time, so as to balance the energy storage SOC state in a better state while meeting the power forecast assessment; The method specifically comprises the following steps: Step 1) Obtain the short-term power prediction value P corresponding to the current moment from the photovoltaic power prediction system fd and ultra-short-term power prediction value P fc ; Obtain the maximum discharge power P1 and maximum charging power P2 currently allowed by the energy storage system; Obtain the power generation power P of the photovoltaic station; Step 2) Determine the adjustment range A allowed for short-term power forecasting: [P fd,min P fd,max ], determine the allowable adjustment range B of ultra-short-term power forecast as: [P fc,min P fc,max ], the power regulation range C of photovoltaic and energy storage systems is determined as: [P min P max ]; Step 3) Determine the adjustment range of the energy storage system [P a P b ]; Step 4) Determine the energy storage system power command P according to the energy storage system power adjustment range and the energy storage system SOC state MW , while meeting the power prediction assessment, try to balance the energy storage SOC state in a better state; The step 4 is specifically as follows: 401) If the energy storage system is charged, that is, P b ≤0, then 402) If the energy storage system discharges, that is, P a ≥0, then 403) If the energy storage system can be discharged and charged, that is, P a <0andP b >0, then SOC is the state of charge of the energy storage system.

2. A photovoltaic power prediction and control method based on an energy storage system according to claim 1, characterized in that: The boundary value of the adjustment range in step 2 is specifically calculated as follows: in; ΔP d ΔP is the allowable control deviation for short-term power prediction; c Allowable control deviation for ultra-short-term power prediction.

3. A photovoltaic power prediction and control method based on an energy storage system according to claim 1, characterized in that: The step 3 is specifically as follows: 301) If satisfied Then P a =P b =0; 302) If satisfied but, 303) If satisfied but, 304) If satisfied but .

4. A photovoltaic power prediction and control method based on an energy storage system according to claim 3, characterized in that: Said Specifically: if the photovoltaic energy storage system cannot control the short-term and ultra-short-term power forecast errors within the allowable range, the energy storage system will not participate in power regulation.

5. The photovoltaic power prediction and control method based on the energy storage system according to claim 3 is characterized in that: Said Specifically, the photovoltaic storage system can only control the ultra-short-term power prediction error within the allowable range, so the energy storage system participates in the ultra-short-term power prediction error control.

6. A photovoltaic power prediction and control method based on an energy storage system according to claim 3, characterized in that: Said Specifically: the photovoltaic storage system can control the short-term power forecast error within the allowable range, but the adjustment range of short-term power forecast and ultra-short-term power forecast do not overlap. The photovoltaic storage system cannot meet both requirements at the same time, so priority is given to short-term power forecast error control, which has a higher assessment cost.

7. A photovoltaic power prediction and control method based on an energy storage system according to claim 3, characterized in that: Said Specifically: if the photovoltaic storage system can simultaneously control the short-term and ultra-short-term power prediction errors within the allowable range, the energy storage system will coordinate the control of the short-term and ultra-short-term power prediction errors.

8. The photovoltaic power prediction and control method based on the energy storage system according to claim 2 is characterized in that: When the short-term power forecast, ultra-short-term power forecast and actual power of the photovoltaic system at night are all less than ΔP c When the power regulation lower limit of the photovoltaic storage system is: P min =max(P+P2,0), reducing the amount of electricity offline at the photovoltaic station without affecting the power forecast assessment.

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