Energy storage dynamic optimization scheduling method and system of power system and storage medium

Abstract

The invention relates to the field of electric power systems, in particular to an energy storage dynamic optimization scheduling method and system of an electric power system and a storage medium, and the method comprises the steps: S1, building an energy storage dynamic calculation model; s3, energy consumption models of a water chilling unit, an air source heat pump, a water pump and a cooling tower are established respectively, and instantaneous energy consumption under different working conditions is calculated by considering operation working conditions and environmental parameter correction; and S4, outputting the utilization rate, the maximum adjusting power, the average adjusting power, the maximum adjusting capacity proportion and the accumulative adjusting electric quantity proportion of the energy storage device, and carrying out quantitative evaluation. According to the method, supply and demand state changes such as valley load, flat load, peak load, peak load and the like of the power system can be identified in real time / quasi-real time, the time periods and power of energy storage and energy release are adjusted, dynamic power distribution of a host in each load time period is realized, and the overall energy efficiency level and operation stability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method, system and storage medium for dynamic optimization scheduling of energy storage in a power system. Background Art

[0002] In existing technologies, building air conditioning energy storage systems generally adopt an operation scheduling strategy based on fixed time periods. Typically, the main unit is activated during a preset nighttime period to store cold or heat, and fixed storage and release times are set. This approach assumes a relatively stable electricity supply and demand structure and fails to fully account for intraday fluctuations and seasonal differences in electricity load, resulting in significant limitations in practical applications. On the one hand, the strategy is highly rigid and cannot dynamically adjust the storage power and duration based on real-time power conditions and building load, lacking the ability to adapt to hourly load changes throughout the year. On the other hand, existing scheduling models do not consider the impact of load rate on the main unit's operating energy efficiency, nor do they incorporate the real-time impact of meteorological parameters (such as temperature and humidity) on main unit performance. This can cause the main unit to frequently operate in inefficient conditions, leading to deviations in the estimated main unit energy consumption, thus affecting the accurate assessment of the system's flexible adjustment capabilities and overall energy efficiency optimization. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, system and storage medium for dynamic optimization scheduling of energy storage in a power system, so as to maximize the utilization efficiency of the energy storage device and enhance the flexible regulation capability of the system.

[0004] In a first aspect, the present invention provides a method for dynamically optimizing energy storage scheduling in a power system, comprising the following steps: S1: Establishing a dynamic calculation model for energy storage: Input operating parameters, including: rated operating capacity of air conditioner host w , the maximum storage capacity of the energy storage device X max , the number of hours of off-peak hours during the energy storage period T 1. Normal hours T 2. The cumulative load during off-peak hours exceeds the host capacity Y 1. The cumulative load exceeds the host capacity during normal periods Y 2. Cumulative load during peak hours Z 1. Accumulated load Z2 during normal period; calculate the time required for the main engine to run at full capacity and complete full energy storage T 0= X max / w ; when T 0≤ T 1, the system does not need to store energy in the flat section; when T 0>T 1, determine whether the system needs to store energy in the flat section; Output the actual energy storage in the valley section X t1 And the actual energy storage in the flat section X t2 ; S2: Based on the host energy efficiency curve and the load rate efficient interval, control the operation capacity of the host during energy storage: Input host performance curve data and hourly meteorological parameters; According to the host performance curve, determine the upper limit of the load rate interval corresponding to the high energy efficiency operation n , calculate the upper limit of the high efficiency operation power of the host in the load rate interval: w h =w*n ; According to the host performance curve and the hourly meteorological conditions, calculate the outdoor meteorological parameter correction coefficient of the host operation at each time k ci ; When X t2 =0, it means that there is no need to store energy in the flat section, and only the low valley section needs to complete energy storage; determine whether the host can complete energy storage in the low valley section according to w h , if yes, the maximum energy storage power in the low valley section is w h , otherwise the maximum energy storage power in the low valley section is w ; When X t2 ≠0, it means that it is necessary to participate in energy storage in the flat section, and the maximum energy storage power in the low valley section is w ; determine whether the host can complete energy storage in the energy storage flat section according to w h , if yes, the maximum energy storage power in the flat section is w h , otherwise the maximum energy storage power in the flat section is w ; Output the host energy storage power in each section; S3: Establish energy consumption models of chiller, air source heat pump, water pump and cooling tower respectively, consider operation conditions and environmental parameter correction, and calculate instantaneous energy consumption under different conditions; S4: Output the utilization rate of energy storage device, maximum regulation power, average regulation power, maximum regulation capacity ratio, and cumulative regulation power ratio for quantitative evaluation.

[0005] Preferably, in S3, the chiller energy consumption model is:

[0006]

[0007]

[0008]

[0009]

[0010] In the formula: is i is the refrigeration performance coefficient of the chiller unit direct supply mode at the moment; is the refrigeration coefficient under standard conditions when the chiller unit is directly supplied; is i is the refrigeration performance coefficient of the chiller unit cold storage mode at the moment; is the refrigeration coefficient under standard conditions when the chiller unit is cold stored; is i is the outdoor parameter correction coefficient of the chiller unit at the moment; is i is the cooling water inlet temperature at the moment, unit: ℃; is i is the main unit part load rate correction coefficient of the chiller unit direct supply mode at the moment; is i is the main unit part load rate correction coefficient of the chiller unit cold storage mode at the moment; a 1, b 1, c 1, d 1is the correction curve coefficient of the chiller unit, respectively; is i is the chiller unit direct supply refrigeration energy consumption at the moment, unit: kW; is i is the chiller unit direct supply cooling load at the moment, unit: kW; is i is the chiller unit cold storage energy consumption at the moment, unit: kW; is i is the chiller unit cold storage load at the moment, unit: kW.

[0011] Preferably, the air source heat pump energy consumption model is:

[0012]

[0013]

[0014]

[0015]

[0016] wherein: COPair i COPair COPair COPair i COPair COPair COPair i COPair COPair i COPair COPair i COPair COPair i Tdb a 2、 b 2、 c 2、 d 2、 COPair i Eair Qair i Qair Eair i Eair Qair i Qair The water pump energy consumption model is:

[0017] wherein: Ew i Ew Qw i Qw 3 Hw i Hw ηw The cooling tower energy consumption model is:

[0018] wherein: Ect i Ect ​The power consumption of the cooling tower per unit cooling capacity.

[0019] Preferably, the constraints of the energy consumption model of the chiller, air source heat pump, water pump and cooling tower include host output power constraints, energy storage capacity constraints and load balance constraints.

[0020] Preferably, the constraints of the energy consumption model of the chiller, air source heat pump, water pump, and cooling tower are:

[0021] Where: for i Energy stored at any moment, unit: kW; w is the rated operating capacity of the air conditioner host, unit: kW; The number of energy storage hours for the day, unit: h; The storage capacity of the energy storage device when it is at full capacity, unit: kW·h; for i Energy directly supplied by the host at any moment, unit: kW; for i Energy storage equipment supply energy at any moment, unit: kW; for i The building load at that moment, unit: kW.

[0022] Preferably, in S4, the energy storage device utilization rate SUR The calculation formula is:

[0023] Where: is the energy released by the energy storage device on day j, unit: kW·h.

[0024] Preferably, in S4, the maximum adjustment capacity ratio The calculation formula is:

[0025] In S4, the cumulative adjustment power ratio The calculation formula is:

[0026] Where: is the maximum adjustment capacity ratio; For the system t The regulating capacity at the moment, unit: kW; The reference power of the system when it is not adjusted at the same time, unit: kW; Adjust the cumulative power ratio for the system; T It is the system adjustment period, unit: h.

[0027] Preferably, in S3, the operating condition considered includes direct supply or energy storage, and the environmental parameter includes cooling water temperature or outdoor dry bulb temperature.

[0028] In a second aspect, the present application provides an air conditioning energy storage dynamic optimization scheduling system of a power system, which adopts any of the energy storage dynamic optimization scheduling methods of the power system.

[0029] In a third aspect, the present application provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute any of the air conditioning energy storage dynamic optimization scheduling methods when the computer program is running.

[0030] Compared with the prior art, the present application has the following beneficial effects: The present application can perform energy storage and release scheduling according to dynamic changes of power supply and demand relationship, does not depend on a fixed "night valley and day peak" mode, but identifies power system supply and demand state changes (low load, flat load, peak load, sharp peak load, etc.) in real time / quasi real time, adjusts the time period and power of energy storage and release, realizes dynamic power distribution of the main machine in each load period, preferentially guarantees energy storage of the main machine in a high energy efficiency interval, effectively improves the overall energy efficiency level and operation stability of the system by avoiding the working condition with low COP of the main machine, and establishes a scheduling optimization model taking annual hourly load prediction and meteorological conditions as inputs. The model can dynamically correct the operation strategy in combination with the influence of meteorological parameters on the performance of the main machine, and ensure that the scheduling result has adaptability and superior performance under all-year, multi-climate conditions. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 It is a flowchart of the system energy storage optimization scheduling method.

[0032] Figure 2 It is a flowchart of the main machine high-efficiency COP energy storage optimization method.

[0033] Figure 3 It is a schematic diagram of energy storage and release scheduling of the energy storage system on a typical day in the cooling supply season.

[0034] Figure 4 It is a schematic diagram of hourly power of the air conditioning system on a typical day in the cooling supply season.

[0035] Figure 5 It is a schematic diagram of energy storage and release scheduling of the energy storage system on a typical day in the heating supply season.

[0036] Figure 6 It is a schematic diagram of hourly power of the air conditioning system on a typical day in the heating supply season. DETAILED DESCRIPTION

[0037] The application will be described in further detail below with reference to the embodiments. However, it should not be understood that the scope of the above-mentioned subject matter of the application is limited to the following embodiments, and any technology achieved based on the content of the application falls within the scope of the application.

[0038] In the description of the embodiments of the application, the terms indicating the orientation or positional relationship of "up", "down", "left", "right", "center", "inner", "outer", etc. are expressed based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship when the product / device / apparatus is usually used. These terms of orientation or positional relationship are only for the convenience of describing the application scheme or simplifying the description in the embodiments, so as to enable the skilled person to quickly understand the scheme, and should not be understood as indicating or implying that a specific device / component / element must have a specific orientation or be constructed and operated in a specific positional relationship, and therefore cannot be understood as a limitation on the application.

[0039] In addition, if the terms "horizontal", "vertical", "overhanging", "parallel", "coaxial" and the like appear, it does not mean that the corresponding device / component / element must be absolutely horizontal or vertical or overhanging or parallel or coaxial, but can be slightly inclined or have a deviation, as long as it does not affect the normal function of the related component. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined; "coaxial" means that two components are arranged as coaxial as possible, and are moved in a coaxial or approximately coaxial manner when the relative position changes. Alternatively, it can be simplified to understand that the corresponding device / component / element is arranged in the "horizontal", "vertical", "overhanging", "parallel", "coaxial" direction, and can have an error / deviation of ±10% relative to the corresponding direction, more preferably an error / deviation of ±8% or less, more preferably an error / deviation of ±6% or less, more preferably an error / deviation of ±5% or less, and more preferably an error / deviation of ±4% or less. For example, the deviation in the "coaxial" direction is controlled within 0.2-1mm, preferably within 0.2-0.5mm. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its role in the application scheme.

[0040] In addition, the terms "first", "second", "third" and the like appearing in the terms are only used to distinguish the description of the same or similar components, and should not be understood as emphasizing or implying the relative importance of the specific components.

[0041] In addition, in the description of the embodiments of the application, "several", "a plurality of", "several" represent at least 2. It can be 2, 3, 4, 5, 6, 7, 8, 9, etc. in any case, and even more than 9.

[0042] Further, in the description of the technical solutions of the present application, unless otherwise explicitly specified / limited / limited, the terms "arrangement", "installation", "connection", "connection", "provided with", "laid", "arrangement" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrally connected, which can be welding, riveting, bolting, screwing and other commonly used connection means in the art. The connection can be mechanical connection, electrical connection or communication connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication between two elements.

[0043] Embodiment 1 A dynamic optimization scheduling method of an energy storage system, comprising the following steps: S1: Establishing a dynamic calculation model of energy storage, as shown in Figure 1 , giving priority to fully charging energy storage during the low valley period. If the cumulative load is high on the day and the main machine cannot complete the target energy storage capacity during the low valley period, the system should strategically introduce flat section to supplement energy storage to maximize the utilization efficiency of energy storage devices, specifically: 1. Input operating parameters, including: air conditioner main machine rated operating capacity w , maximum energy storage capacity of energy storage device X max , number of hours of low valley period in energy storage period T 1. Number of hours of flat period T 2, cumulative load of valley period exceeding main machine capacity Y 1, cumulative load of flat period exceeding main machine capacity Y 2, cumulative load of peak period Z 1, cumulative load of flat period Z2; calculate the time required for the main machine to run at full capacity to complete full energy storage T 0= X max / w ; 2, when T 0≤ T 1, the system can fully charge the energy storage device during the low valley period, without the need for energy storage in the flat section. The energy storage capacity meets the peak, flat and valley period loads of the next day, and the maximum does not exceed the capacity of the energy storage device. 3, when T 0> T 1, it is necessary to determine whether the flat section needs to be charged; if the main machine runs at full capacity for T 1 hour during the energy storage period, it cannot meet the cumulative load of the next day's valley and flat periods exceeding the main machine capacity and the cumulative load of the peak period, so the system needs to charge in the flat section. 4. Finally output the actual energy storage capacity of the valley period according to the energy storage operation logicX t1 actual energy storage X t2 .

[0044] when T 0≤ T 1, and Y 1+ Z 1+ Z 2≥ X max then X t1 = X max , X t2 =0; when T 0≤ T 1, and Y 1+ Z 1+ Z 2< X max then X t1 = Y 1+ Z 1+ Z 2, X t2 =0; when T 0> T 1, and Y 1+ Y 2+ Z 1≤ w T 1, and Y 1+ Z 1+ Z 2≤ w T 1 then X t1 = Y 1+ Z 1+ Z 2, X t2 =0; when T 0> T 1, and Y 1+ Y 2+ Z 1≤ w T 1, and Y 1+ Z 1+ Z 2> w T 1 then X t1 = w T 1, X t2 =0; When T 0 T 1, and Y 1 Y 2 Z 1 w T 1, and Y 1 Y 2 Z 1 X max Then X t1 = w T 1 X t2 = Y 1 Y 2 Z 1 - w T 1 When T 0 T 1, and Y 1 Y 2 Z 1 w T 1, and Y 1 Y 2 Z 1 X max Then X t1 = w T 1 X t2 = X max - w T 1

[0045] S2: The host efficient COP energy storage logic is shown in Figure 2 Based on the host energy efficiency curve and the load rate efficient interval, the running capacity of the host energy storage is controlled, specifically: 1, Input parameter setting: the running capacity of the air conditioner host w , the low valley period hours of the energy storage period T 1, the normal period hours T 2, the actual energy storage capacity of the valley segment X t1 and the actual energy storage capacity of the normal segment X t2 , the host performance curve data and the hourly meteorological parameters; 2, determine the host efficient operation interval: according to the host performance curve, determine the corresponding load rate interval upper limit of its high energy efficiency operation n , calculate the upper limit of the high efficient operation power of the host in the load rate interval: w h =w*n ; 3. Calculate the correction coefficient of the host's outdoor meteorological parameters at each moment k ci Value: Calculate the outdoor meteorological parameter correction coefficient at each moment when the host is running based on the host performance curve and hourly meteorological conditions. k ci , used for subsequent energy efficiency ranking and power allocation; 4. When X t2 =0, it means that there is no need to store energy during normal periods, and only energy needs to be stored during off-peak periods; w h Can the energy storage be completed during operation? If so, the maximum energy storage power during the off-peak period is w h Otherwise, the maximum energy storage power during the valley period is w ; 5. When X t2 ≠0, indicating that energy storage is required during normal periods, and the maximum energy storage power during off-peak periods is w ; Determine whether the host is pressing w h Can the energy storage be completed during operation? If so, the maximum energy storage power during normal period is w h , otherwise the maximum energy storage power in normal period is w ; 6. Output the host energy storage power in each period; S3: Establish energy consumption models for the chiller, air source heat pump, water pump, and cooling tower respectively, taking into account the operating conditions (direct supply / energy storage) and environmental parameters (cooling water temperature, outdoor dry-bulb temperature) corrections, and calculate the instantaneous energy consumption under different operating conditions; The energy consumption model of the chiller is:

[0046]

[0047]

[0048]

[0049]

[0050] Where: for i The cooling performance coefficient of the chiller in direct supply condition at the moment; is the cooling coefficient under standard working conditions when the chiller is directly supplied; for i Refrigeration performance coefficient of the chiller in cold storage condition at all times; The cooling coefficient of the chiller under standard operating conditions when storing cold; for i The outdoor parameter correction coefficient of the chiller at the time can only consider the influence of cooling water temperature to simplify the calculation; for i Cooling water inlet temperature at the moment, unit: ℃; for i Correction coefficient of host partial load rate of direct supply condition of chiller at the moment; for i Correction coefficient of host partial load rate of chiller in cold storage condition at the moment; a 1. b 1. c 1. d 1 are the correction curve coefficients of the chiller, which are obtained by fitting the sample parameters of products from mainstream equipment manufacturers; for i The direct cooling energy consumption of the chiller at any moment, unit: kW; for i Direct cooling load of chiller at any moment, unit: kW; for i The energy consumption of cold storage of chiller at each moment, unit: kW; for i The cooling load of the chiller at that moment, unit: kW.

[0051] The energy consumption model of the air source heat pump is:

[0052]

[0053]

[0054]

[0055]

[0056] Where: for i The performance coefficient of the air source heat pump direct supply condition at the moment; The rated cooling / heating coefficient of performance under direct supply conditions of the air source heat pump; for i The performance coefficient of the air source heat pump energy storage condition at the moment; The rated cooling / heating coefficient of performance of the air source heat pump under energy storage conditions; for i Correction coefficient of outdoor meteorological parameters of air source heat pump at all times; for iThe air source heat pump direct supply working condition host part load rate correction coefficient at the moment; For i The air source heat pump energy storage working condition host part load rate correction coefficient at the moment; For i The outdoor dry bulb temperature at the moment, unit: ℃; a 2, b 2, c 2, d 2Air source heat pump correction curve coefficient, correction curve is obtained according to the product sample parameter fitting of main equipment manufacturer; For i The air source heat pump direct supply energy consumption at the moment, unit: kW; For i The air source heat pump direct supply load at the moment, unit: kW; For i The air source heat pump energy storage energy consumption at the moment, unit: kW; For i The air source heat pump energy storage load at the moment, unit: kW.

[0057] The water pump energy consumption model is:

[0058] In the formula, For i The water pump energy consumption at the moment, unit: kW; For i The water pump flow at the moment, unit: m 3 / h; For i The water pump head at the moment, unit: mH2O; The water pump comprehensive efficiency.

[0059] The cooling tower energy consumption model is:

[0060] In the formula, For i The cooling tower energy consumption at the moment, unit: kW; The unit cooling tower power consumption.

[0061] The constraint conditions of the energy consumption model of the chiller, air source heat pump, water pump and cooling tower include host output power constraint, energy storage capacity constraint and load balance constraint. The constraint conditions of the energy consumption model of the chiller, air source heat pump, water pump and cooling tower are:

[0062] In the formula, For iEnergy stored at any moment, unit: kW; w is the rated operating capacity of the air conditioner host, unit: kW; The number of energy storage hours for the day, unit: h; The storage capacity of the energy storage device when it is at full capacity, unit: kW·h; for i Energy directly supplied by the host at any moment, unit: kW; for i Energy storage equipment supply energy at any moment, unit: kW; for i The building load at that moment, unit: kW.

[0063] S4: Output energy storage device utilization rate, maximum regulation power, average regulation power, maximum regulation capacity ratio, and cumulative regulation power ratio, which are used to quantitatively evaluate the dynamic optimization scheduling method.

[0064] The energy storage device utilization rate (SUR) is the ratio of the total energy discharge actually used to meet the load throughout the year to the rated available capacity of the energy storage device. This measures capacity utilization; a higher SUR indicates a higher energy storage device utilization rate.

[0065] The energy storage device utilization rate SUR The calculation formula is:

[0066] Where: is the energy released by the energy storage device on day j, unit: kW·h.

[0067] The maximum regulation capacity ratio and the cumulative regulation power ratio are used to evaluate the system flexibility effect. The larger the index, the better the system's flexible regulation capability and effect.

[0068] The maximum adjustment capacity ratio The calculation formula is:

[0069] In S4, the cumulative adjustment power ratio The calculation formula is:

[0070] Where: is the maximum adjustment capacity ratio; For the system t The regulating capacity at the moment, unit: kW; The reference power of the system when it is not regulated at the same time, unit: kW; Adjust the cumulative power ratio for the system; T It is the system adjustment period, unit: h.

[0071] The optimization scheduling method provided by the application comprises real-time / quasi-real-time identification of power grid load characteristics, combination of building air conditioner load prediction, dynamic determination of energy storage / discharge period and power distribution, dynamic power distribution and operation timing optimization method between main machine direct supply and energy storage device, which comprises optimal matching scheduling based on main machine COP curve and load rate, and the main machine is preferentially operated at high load rate in the energy storage period. The optimization scheduling method provided by the application comprises real-time / quasi-real-time identification of power grid load characteristics, combination of building air conditioner load prediction, dynamic determination of energy storage / discharge period and power distribution, dynamic power distribution and operation timing optimization method between main machine direct supply and energy storage device, which comprises optimal matching scheduling based on main machine COP curve and load rate, and the main machine is preferentially operated at high load rate in the energy storage period.

[0072] The application can solve the technical problems of low utilization rate of energy storage equipment and unreleased grid regulation potential, and the method has strong adaptability and can effectively support grid peak clipping and renewable energy consumption.

[0073] Embodiment 2 Based on embodiment 1, the present embodiment provides a specific application example. In the cooling supply season, May 1, June 30 and July 20 are selected as typical days of low, medium and high load respectively. The specific energy storage and discharge of the system and the hourly power are shown in FIG. 3 and FIG. 4. On May 1, the air conditioner cooling load is small, and the daily cumulative cooling load is less than the rated energy of the energy storage device, so the energy storage device is not full, the energy storage time is 5.1h, and the cooling load of the whole day is borne by the energy storage device; the transferred electricity of the building use period is 1863 kW·h, and the transfer rate is 90%. On June 30, the air conditioner cooling load is moderate, the energy storage device is full, the energy storage time is 5.5h, and the building cooling load is borne by the energy storage device and the cooling main machine; the transferred electricity of the building use period is 2827 kW·h, and the transfer rate is 52%. On July 20, the air conditioner cooling load is large, the energy storage device is full, the energy storage time is 5.5h, and the building cooling load is borne by the energy storage device and the cooling main machine; the transferred electricity of the building use period is 3315 kW·h, and the transfer rate is 40%.

[0074] In the heating season, December 5, December 22 and January 1 are selected as typical days of low, medium and high load. The specific heat storage and release of the system and the hourly power are shown in FIG. 5 and FIG. 6. On December 5, the air conditioning heat load is small, and the 4-hour energy storage in the valley period can cover the load of the next day's peak, high peak and most of the flat period, so only in the valley period, the building heat load is borne by the energy storage device and the air source heat pump; the building uses the transferred power of 979 kW·h in the period, and the transfer rate is 67%. On December 22, the building heat load is moderate, and the 4-hour energy storage in the valley period can cover the load of the next day's peak, high peak and a small amount of flat period, so only in the valley period, the building heat load is borne by the energy storage device and the air source heat pump; the building uses the transferred power of 973 kW·h in the period, and the transfer rate is 38%. On January 1, the building heat load is large, and on the basis of 4 hours of storage in the valley period, 1.85 hours of energy storage is supplemented in the flat period to meet the demand of continuous high load during the day, the building heat load is borne by the energy storage device and the air source heat pump, and the energy storage device does not release heat in the flat period; the building uses the transferred power of 1519 kW·h in the period, and the transfer rate is 37%.

[0075] The utilization rate of the energy storage device of the energy storage system in the cooling season is 44%, and in the heating season is 12%.

[0076] The flexibility effect of the energy storage system in the peak period of the whole year is shown in Table 1: Table 1 Flexibility effect of energy storage system in peak period of the whole year

[0077] Embodiment 3 An air conditioning energy storage dynamic optimization scheduling system of a power system, which adopts the energy storage dynamic optimization scheduling method of the power system as any one of Embodiment 1 or Embodiment 2.

[0078] Embodiment 4 A computer readable storage medium, comprising a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the air conditioning energy storage dynamic optimization scheduling method as described in Embodiment 1 or Embodiment 2.

[0079] The above only describes the preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dynamic optimal dispatch of energy storage in a power system, characterized in that, The method comprises the following steps: S1: establishing an energy storage dynamic calculation model: Input operating parameters, including: air conditioning host rated operating capacity w , maximum energy storage device energy storage X max , low valley period of energy storage period hours T 1, flat period hours T 2, cumulative load of valley period exceeding host capacity Y 1, cumulative load of flat period exceeding host capacity Y 2, peak period cumulative load Z 1, flat period cumulative load Z2; calculate the time required for host full capacity operation to complete full energy storage T 0= X max / w ; When T 0≤ T 1, the system does not need to store energy in the flat section; When T 0 T 1, determine whether the system needs to store energy in the flat section; output valley actual energy storage X t1 peaceful section actual energy storage X t2 ; S2: controlling the operation capacity of the host energy storage based on the host energy efficiency curve and the load rate high efficiency interval: Input the host performance curve data and hourly meteorological parameters; According to the host performance curve, determine the upper limit of the load rate interval corresponding to the high energy efficiency operation of the host n , calculate the upper limit of the high efficiency operation power of the host in the load rate interval: w h = w * n ; According to the host performance curve and the hourly meteorological conditions, the outdoor meteorological parameter correction coefficient of the host running at each time is calculated k ci ; When X t2 =0, it means that there is no need to store energy during the normal period, only to complete the valley period energy storage; to determine the host in the valley period according to w h whether the operation can complete the energy storage, if yes, the maximum value of the valley period energy storage power is w h , otherwise the maximum value of the valley period energy storage power is w ; When X t2 ≠ 0, it means that the energy storage needs to be involved in the flat period, and the maximum energy storage power in the valley period is w ; the host determines whether the energy storage can be completed in the flat period, and if so, the maximum energy storage power in the flat period is w h ; otherwise, the maximum energy storage power in the flat period is w h w ;​ Output the host energy storage power in each period; S3: respectively establishing energy consumption models of the chiller, air source heat pump, water pump and cooling tower, considering the operation condition and environmental parameter correction, and calculating the instantaneous energy consumption under different working conditions; S4: outputting the energy storage device utilization rate, maximum adjustment power, average adjustment power, maximum adjustment capacity proportion and cumulative adjustment power proportion for quantitative evaluation.

2. The method of claim 1, wherein, In S3, the chiller energy consumption model is: In the formula: COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; a 1, b 1, c 1, d 1, COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; i COP is the cooling capacity coefficient of the chiller unit at the time of direct supply; 3. The energy storage dynamic optimization scheduling method of the power system according to claim 2, characterized in that, The air source heat pump energy consumption model is: In the formula: COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; i COPair, r is the rated cooling / heating performance coefficient of the air source heat pump in the direct supply working condition; COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; COPair, r is the rated cooling / heating performance coefficient of the air source heat pump in the direct supply working condition; i COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; COPair, r is the rated cooling / heating performance coefficient of the air source heat pump in the direct supply working condition; COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; i COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; i COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; i COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; COPair, t is the air source heat pump direct supply working condition performance coefficient at time t; i Tdb, t is the outdoor dry-bulb temperature at time t, unit: ℃; a 2、 b 2、 c 2、 d 2respectively air source heat pump correction curve coefficient; is i air source heat pump direct supply energy consumption at time t, unit: kW; is i air source heat pump direct supply load at time t, unit: kW; is i air source heat pump energy storage energy consumption at time t, unit: kW; is i air source heat pump energy storage load at time t, unit: kW; The water pump energy consumption model is: In the formula: is i the water pump energy consumption at the moment, unit: kW; is i the water pump flow at the moment, unit: m 3 / h; is i the water pump head at the moment, unit: mH2O; is the comprehensive efficiency of the water pump; The cooling tower energy consumption model is: In the formula: is i Cooling tower energy consumption at the moment: kW; is the unit cooling capacity cooling tower power consumption.

4. The method of claim 3, wherein, The constraint conditions of the energy consumption models of the chiller, air source heat pump, water pump and cooling tower include the host output power constraint, energy storage capacity constraint and load balance constraint.

5. The method of claim 4, wherein, The constraint conditions of the energy consumption models of the chiller, air source heat pump, water pump and cooling tower are: In the formula: is i Energy storage at the moment, unit: kW; w is the rated operating capacity of the air conditioner host, unit: kW; is the energy storage hours of the day, unit: h; is the energy storage capacity of the energy storage device, unit: kW·h; is i is the direct energy supply of the host at the moment, unit: kW; is i is the energy supply of the energy storage device at the moment, unit: kW; is i is the building load at the moment, unit: kW.

6. The method of claim 5, wherein, In the S4, the energy storage device utilization rate SUR The calculation formula is: In the formula: is the discharging energy of the energy storage device on the jth day, in units of kW·h.

7. The method of claim 6, wherein, In the S4, the maximum adjustment capacity ratio The calculation formula is: In the S4, the cumulative adjustment power proportion The calculation formula is: In the formula: is the maximum regulation capacity ratio; is the regulation capacity of the system at t time, unit: kW; is the reference power of the system at the same time without regulation, unit: kW; is the cumulative regulation power ratio of the system; T is the system regulation period, unit: h.

8. The method for dynamic optimal dispatch of energy storage of power system according to any one of claims 1-7, characterized in that, In S3, the operation condition includes direct supply or energy storage, and the environmental parameters include cooling water temperature or outdoor dry bulb temperature.

9. An air-conditioning energy storage dynamic optimization scheduling system of a power system, characterized in that, The energy storage dynamic optimization scheduling method of the power system according to any one of claims 1-8 is adopted.

10. A computer readable storage medium, characterized in that, A computer program is stored, wherein when the computer program runs, the device where the computer readable storage medium is located is controlled to execute the air conditioner energy storage dynamic optimization scheduling method according to any one of claims 1-8.

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