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

Abstract

The present application relates to the field of power system, specifically relates to a kind of energy storage dynamic optimization scheduling method, system and storage medium of power system, the method includes S1: establishing energy storage dynamic calculation model: S2: based on host energy efficiency curve and load rate high efficiency interval, control the operation capacity of host energy storage time: S3: respectively establish the energy consumption model of water chiller unit, air source heat pump, water pump, cooling tower, consider operating condition and environmental parameter correction, calculate the instantaneous energy consumption under different operating conditions;S4: output energy storage device utilization, maximum regulation power, average regulation power, maximum regulation capacity proportion, cumulative regulation power proportion, carry out quantitative evaluation.The present application can identify power system supply and demand state change in real time / quasi real time: valley load, flat section load, peak load, sharp peak load and the like, adjust the period and power of energy storage and energy release, realize the dynamic power distribution of host in each load period, improve the overall energy efficiency level and operating stability of system.

Description

TECHNICAL FIELD

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

[0002] In the prior art, the building air conditioning energy storage system generally adopts an operation scheduling strategy based on a fixed time period, usually starting the main unit to carry out cold or heat storage in the preset night period, and setting a fixed energy storage and release time. This method is based on the assumption that the power supply and demand structure is relatively stable, and does not fully consider the intra-day fluctuation and seasonal difference of power load, resulting in obvious limitations in actual application. On the one hand, the strategy is rigid and cannot dynamically adjust the energy storage power and time according to real-time power conditions and building load, lacking the ability to adapt to the hourly load changes throughout the year; on the other hand, the existing scheduling model does not consider the influence of load rate on the energy efficiency of the main unit, nor does it take into account the real-time influence of meteorological parameters (such as temperature and humidity) on the performance of the main unit, causing the main unit to run in low efficiency condition frequently, and causing the energy consumption estimation of the main unit to deviate, thereby affecting the accurate evaluation of the flexible regulation ability of the system and the overall energy efficiency optimization. SUMMARY

[0003] The present application aims to overcome the deficiencies in the prior art, and provides a power system energy storage dynamic optimization scheduling method, system and storage medium, which maximizes the utilization efficiency of the energy storage device and improves the flexible regulation ability of the system.

[0004] In a first aspect, the present application provides a power system energy storage dynamic optimization scheduling method, comprising the following steps:

[0005] S1: Establishing an energy storage dynamic calculation model:

[0006] Input operating parameters, including: rated operating capacity of the air conditioner main unit w , maximum energy storage capacity of the energy storage device X max , number of hours of low valley period of energy storage period T 1, number of hours of flat period T 2, cumulative load of valley period exceeding main unit capacity Y 1, cumulative load of flat period exceeding main unit capacity Y 2, cumulative load of peak period Z 1, cumulative load of flat period Z2; calculate the time required for the main unit to run at full capacity to complete full energy storage T 0= X max / w ;

[0007] When T 0≤ T1, the system does not need to store energy in flat section;

[0008] When T 0> T 1, determine whether the system needs to store energy in flat section;

[0009] Output the actual energy storage in the valley section X t1 And the actual energy storage in the flat section X t2 ;

[0010] S2: Based on the host energy efficiency curve and the load rate efficient interval, control the operation capacity of the host when storing energy:

[0011] Input the host performance curve data and the hourly meteorological parameters;

[0012] According to the host performance curve, determine the upper limit of the load rate interval corresponding to its 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 ;

[0013] According to the host performance curve and the hourly meteorological conditions, calculate the outdoor meteorological parameter correction coefficient of the host when running at each time k ci ;

[0014] When X t2 =0, it means that there is no need to store energy in flat section, only to complete the energy storage in the valley section; determine whether the host can complete the energy storage by running in the valley section w h , if yes, the maximum energy storage power in the valley section is w h , otherwise the maximum energy storage power in the valley section is w ;

[0015] When X t2 ≠0, it means that it needs to participate in energy storage in flat section, and the maximum energy storage power in the valley section is w ; determine whether the host can complete the energy storage by running in the energy storage flat section 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 ;

[0016] Output the host energy storage power in each section;

[0017] S3: Establish energy consumption models of the water 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;

[0018] S4: Output the utilization rate of the energy storage device, maximum adjustment power, average adjustment power, maximum adjustment capacity ratio and cumulative adjustment power ratio for quantitative evaluation.

[0019] Preferably, in S3, the energy consumption model of the water chiller is:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] In the formula: is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; a 1, b 1, c 1, d 1is the correction curve coefficient of the water chiller; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃; is the cooling water inlet temperature at the moment, unit: ℃; i is the cooling water inlet temperature at the moment, unit: ℃.

[0026] Preferably, the energy consumption model of the air source heat pump is as follows:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] In the formula: for i The coefficient of performance (COP) of the air source heat pump under direct supply operating conditions at any given time; The rated cooling / heating performance coefficient under direct air source heat pump operation; for i The coefficient of performance of air source heat pump energy storage at any given time; The rated cooling / heating performance coefficient of the air source heat pump under energy storage conditions; for i Correction factor for outdoor meteorological parameters of air source heat pump at all times; for i Correction factor for the load factor of the main unit in the case of direct supply of air source heat pump at any time; for i Correction factor for the partial load rate of the main unit in the air source heat pump energy storage condition at any time; for i Outdoor dry-bulb temperature at any given time, in °C; a 2. b 2. c 2. d 2 represents the correction curve coefficient for air source heat pumps; for i Energy consumption of air source heat pump direct supply, unit: kW; for i Air source heat pump direct load supply at all times, unit: kW; for i Real-time energy consumption of air source heat pump energy storage, unit: kW; for i Real-time air source heat pump energy storage load, unit: kW;

[0033] The energy consumption model of the water pump is as follows:

[0034]

[0035] In the formula: for iInstantaneous water pump energy consumption, unit: kW; is i Instantaneous water pump flow, unit: m 3 / h; is i Instantaneous water pump head, unit: mH2O; is the comprehensive efficiency of the water pump;

[0036] The energy consumption model of the cooling tower is:

[0037]

[0038] In the formula: is i Instantaneous cooling tower energy consumption, unit: kW; is the unit cooling tower power consumption.

[0039] Preferably, the constraint conditions of the energy consumption models of the chiller, air source heat pump, water pump and cooling tower include main machine output power constraints, energy storage capacity constraints and load balancing constraints.

[0040] Preferably, the constraint conditions of the energy consumption models of the chiller, air source heat pump, water pump and cooling tower are:

[0041]

[0042] In the formula: is i Instantaneous energy storage, unit: kW; w is the rated operating capacity of the main machine of the air conditioner, unit: kW; is the number of energy storage hours of the day, unit: h; is the maximum energy storage of the energy storage device, unit: kW·h; is i Instantaneous direct supply energy of the main machine, unit: kW; is i Instantaneous energy supply of the energy storage device, unit: kW; is i Instantaneous building load, unit: kW.

[0043] Preferably, in S4, the utilization rate of the energy storage device is calculated by the formula: SUR

[0044]

[0045] In the formula: is the discharging energy of the energy storage device on the jth day, unit: kW·h.

[0046] Preferably, in S4, the maximum adjustment capacity ratio is calculated by the formula: ​The calculation formula is:

[0047]

[0048] In S4, the cumulative adjustment of the power ratio The calculation formula is:

[0049]

[0050] In the formula: To adjust the maximum capacity ratio; For the system in t The regulating capacity at any given time, in kW; The reference power of the system at any given moment when it is not adjusted, in kW; The system's cumulative power consumption ratio is adjusted. T The system adjustment period is expressed in hours (h).

[0051] Preferably, in S3, the operating conditions considered include direct supply or energy storage, and the environmental parameters include cooling water temperature or outdoor dry-bulb temperature.

[0052] In a second aspect, the present invention provides a dynamic optimization scheduling system for energy storage in a power system, employing any of the described dynamic optimization scheduling methods for energy storage in a power system.

[0053] In a third aspect, the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform any of the described power system energy storage dynamic optimization scheduling methods.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] This invention enables dynamic energy storage and release scheduling based on changes in power supply and demand, without relying on a fixed "night-valley-day-peak" pattern. Instead, it identifies changes in power system supply and demand (low-valley load, flat-period load, peak load, and extreme-peak load) in real-time or near real-time, adjusting the timing and power of energy storage and release. This achieves dynamic power allocation of the main unit during each load period, prioritizing energy storage in high-efficiency zones and effectively improving the overall energy efficiency and operational stability of the system by avoiding conditions with low COP. A scheduling optimization model is established using hourly load forecasts and meteorological conditions throughout the year as input. The model can dynamically adjust the operating strategy by incorporating the impact of meteorological parameters on main unit performance, ensuring that the scheduling results are adaptable and superior under various climatic conditions throughout the year. Attached Figure Description

[0056] Figure 1 A flowchart for the system's energy storage optimization scheduling method.

[0057] Figure 2 Flow chart of the method for high COP energy storage optimization of host.

[0058] Figure 3 Schematic diagram of energy storage and release scheduling of energy storage system for typical day in cooling season.

[0059] Figure 4 Schematic diagram of hourly power of air conditioning system for typical day in cooling season.

[0060] Figure 5 Schematic diagram of energy storage and release scheduling of energy storage system for typical day in heating season.

[0061] Figure 6 Schematic diagram of hourly power of air conditioning system for typical day in heating season. DETAILED DESCRIPTION

[0062] The application will be further described below in connection with specific embodiments. However, it should be understood that the above-mentioned subject matter of the application is not limited to the following embodiments, and any technology realized based on the content of the application falls within the scope of the application.

[0063] In the description of the specific embodiments of the application, the terms indicating the orientation or positional relationship of "up", "down", "left", "right", "center", "inner", "outer" and the like are expressed based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship of the product / equipment / device of the application when it is usually used. These terms of orientation or positional relationship are only used to facilitate the description of the application scheme or simplify the description in the specific embodiments, so as to facilitate the quick understanding of the scheme by the technicians, and are not intended to indicate or imply that the 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 of the application.

[0064] In addition, if the terms "horizontal", "vertical", "overhanging", "parallel", "coaxial" and the like appear in the terms, it does not mean that the corresponding device / component / element is absolutely horizontal or vertical or overhanging or parallel or coaxial, but can be slightly inclined or deviated, 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 coaxially arranged as much as possible, and move 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" and the like, and can have an error / bias of ±10% relative to the corresponding direction, more preferably an error / bias of ±8% or less, more preferably an error / bias of ±6% or less, more preferably an error / bias of ±5% or less, and more preferably an error / bias 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 / bias range, it can still achieve its role in the present application.

[0065] In addition, the terms "first", "second", "third" and the like in the terms are only used to distinguish the same or similar components for description, and should not be understood as emphasizing or implying the relative importance of the specific components.

[0066] In addition, in the description of the embodiments of the present application, "several", "a plurality of", "several" represent at least 2. It can be 2, 3, 4, 5, 6, 7, 8, 9, etc. Any case, or even more than 9 cases.

[0067] In addition, in the description of the technical solutions of the present application, unless otherwise 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 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; it can be the communication between two elements.

[0068] Embodiment 1

[0069] A dynamic optimization scheduling method of an energy storage system, comprising the following steps:

[0070] S1: establishing a dynamic calculation model of energy storage, such as Figure 1As shown, priority is given to full storage during the low period. If the cumulative load is high and the main machine is running at full capacity during the low period, it is still unable to complete the target storage amount, then the system should strategically introduce flat period supplemental storage to maximize the utilization efficiency of the storage device, specifically:

[0071] 1. Input operating parameters, including: air conditioner main machine rated operating capacity w , maximum storage capacity of storage device X max , number of hours of low period in 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 storage T 0= X max / w ;

[0072] 2, when T 0≤ T 1, the system can fully store the storage device during the low period, without the need for storage in the flat period. The storage capacity meets the peak, flat period load and valley period load exceeding the main machine capacity of the next day, and the maximum does not exceed the storage device capacity;

[0073] 3, when T 0> T 1, it is necessary to determine whether the flat period needs to be stored; if the main machine runs at full capacity for T 1 hour but 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, the system needs to store in the flat period;

[0074] 4. According to the storage operation logic, finally output the actual storage capacity of the valley period X t1 and the actual storage capacity of the flat period X t2 .

[0075] When T 0≤ T 1, and Y 1+ Z 1+ Z 2≥ X max , X t1 = X max , Xt2 = 0;

[0076] When 0 < a < 1, T 0 < a < 1, T 1, and Y 1 < a < 2, Z 1 < a < 2, Z 2 < a < 3, X max then X t1 = 0; Y 1 < a < 2, Z 1 < a < 2, Z 2 < a < 3, X t2 = 0;

[0077] When 0 > a > 1, T 0 > a > 1, T 1 < a < 2, Y 1 < a < 2, Y 2 < a < 3, Z 1 > a > 1, w T 1 > a > 1, Y 1 < a < 2, Z 1 < a < 2, Z 2 > a > 1, w T 2 > a > 1, X t1 = 0; Y 1 < a < 2, Z 1 < a < 2, Z 2 < a < 3, X t2 = 0;

[0078] When 0 > a > 1, T 0 > a > 1, T 1 < a < 2, Y 1 < a < 2, Y 2 < a < 3, Z 1 > a > 1, w T 1 > a > 1, Y 1 < a < 2, Z 1 < a < 2, Z 2 > a > 1, w T 2 > a > 1, X t1 = 0; w T 1 < a < 2, X t2 = 0;

[0079] When 0 > a > 1, T 0 > a > 1, T 1 < a < 2, Y 1 < a < 2, Y 2 < a < 3, Z 1 > a > 1, w T 1 > a > 1, Y 1 < a < 2, Y 1 < a < 2, Z 2 > a > 1, X max then X t1 = 0;w T 1, X t2 = Y 1 Y 2 Z 1 - w T 1

[0080] When T 0 T 1 , and Y 1 Y 2 Z 1 w T 1 , and Y 1 Y 2 Z 1 X max , X t1 = w T 1 , X t2 = X max - w T 1 .

[0081] 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, control the operation capacity when the host energy storage, specifically:

[0082] 1, input parameter setting: the running capacity of the host of the air conditioner 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 flat segment X t2 , the host performance curve data and the hourly meteorological parameters;

[0083] 2, determine the host efficient operation interval: according to the host performance curve, determine the upper limit of the load rate interval corresponding to the high energy efficiency of the host n , calculate the upper limit of the high efficient operation power of the host in the load rate interval: w h = w * n ;

[0084] 3, calculate the outdoor meteorological parameter correction coefficient of the host at each time k ci : according to the host performance curve and the hourly meteorological conditions, calculate the outdoor meteorological parameter correction coefficient of the host at each time k ci , which is used for subsequent energy efficiency sorting and power allocation;

[0085] 4. When X t2 =0 indicates that no energy storage is needed during normal periods, only during off-peak periods; determine if the host operates during off-peak periods. w h If the operation can complete energy storage, then the maximum energy storage power during off-peak hours is: w h Otherwise, the maximum energy storage capacity during off-peak hours is w ;

[0086] 5. When X t2 ≠0, indicating that energy storage is required during normal periods, and the maximum energy storage capacity is during off-peak periods. w ; Determine if the host operates during the normal energy storage period. w h If the operation can complete energy storage, then the maximum energy storage capacity during normal operation is: w h Otherwise, the maximum energy storage capacity during normal operation is w ;

[0087] 6. Output the main unit's energy storage power for each time period;

[0088] S3: Establish energy consumption models for chiller units, air source heat pumps, water pumps, and cooling towers respectively, considering operating conditions (direct supply / energy storage) and environmental parameters (cooling water temperature, outdoor dry bulb temperature) corrections, and calculate instantaneous energy consumption under different operating conditions;

[0089] The energy consumption model of the chiller unit is as follows:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] In the formula: for i The coefficient of performance (COP) of the chiller unit under direct supply conditions at any given time; The coefficient of performance (COP) is the cooling capacity under standard operating conditions when the chiller is directly supplied with water. for i The coefficient of performance (COP) of the chiller unit under cold storage conditions at all times; The coefficient of performance (COP) of a chiller unit under standard operating conditions when storing cold energy. for iThe outdoor parameter correction coefficient of the chiller unit at the moment, for simplifying calculation, only the cooling water temperature influence is considered; For i The cooling water inlet temperature at the moment, unit: ℃; For i The chiller unit direct supply working condition host part load rate correction coefficient at the moment; For i The chiller unit cold storage working condition host part load rate correction coefficient at the moment; a 1, b 1, c 1, d 1, respectively, the chiller unit correction curve coefficient, the correction curve is obtained according to the main stream equipment manufacturer product sample parameter fitting; For i The chiller unit direct supply refrigeration energy consumption at the moment, unit: kW; For i The chiller unit direct supply cold load at the moment, unit: kW; For i The chiller unit cold storage energy consumption at the moment, unit: kW; For i The chiller unit cold storage load at the moment, unit: kW.

[0096] The energy consumption model of the air source heat pump is:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] In the formula: For i The air source heat pump direct supply working condition performance coefficient at the moment; The rated refrigeration / heat performance coefficient of the air source heat pump under the direct supply working condition; For i The air source heat pump cold storage working condition performance coefficient at the moment; The rated refrigeration / heat performance coefficient of the air source heat pump under the cold storage working condition; For i The air source heat pump outdoor meteorological parameter correction coefficient at the moment; For i The air source heat pump direct supply working condition host part load rate correction coefficient at the moment; For iThe air source heat pump 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 storage energy consumption at the moment, unit: kW; For i The air source heat pump storage load at the moment, unit: kW.

[0103] The energy consumption model of the water pump is:

[0104]

[0105] 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.

[0106] The energy consumption model of the cooling tower is:

[0107]

[0108] In the formula: For i The cooling tower energy consumption at the moment, unit: kW; The unit cooling tower power consumption.

[0109] The constraint conditions of the energy consumption models of the chiller, air source heat pump, water pump and cooling tower include host output power constraint, storage capacity constraint and load balance constraint. The constraint conditions of the energy consumption models of the chiller, air source heat pump, water pump and cooling tower are:

[0110]

[0111] In the formula: For i The storage capacity at the moment, unit: kW;w Rated operating capacity of the air conditioner's main unit, unit: kW; The number of hours of energy storage for the day, in hours (h). The maximum energy stored in the energy storage device is expressed in kW·h. for i Energy is supplied directly by the main unit at all times, unit: kW; for i Energy storage devices supply energy at all times, unit: kW; for i Building load at any given time, in kW.

[0112] S4: Output energy storage device utilization rate, maximum regulation power, average regulation power, maximum regulation capacity ratio, and cumulative regulation power ratio, used to quantitatively evaluate the dynamic optimization scheduling method.

[0113] The energy storage device utilization rate (SUR) is the proportion of the total energy released to meet the load throughout the year to the rated available capacity of the energy storage device. It measures the degree of capacity utilization; the higher this indicator, the higher the utilization rate of the energy storage device.

[0114] The energy storage device utilization rate SUR The calculation formula is:

[0115]

[0116] In the formula: The energy released by the energy storage device on day j is expressed in kW·h.

[0117] The system's flexibility is evaluated using two indicators: the maximum regulation capacity ratio and the cumulative regulation power ratio. The larger the indicator, the better the system's flexible regulation capability and effect.

[0118] The maximum adjustment capacity ratio The calculation formula is:

[0119]

[0120] In S4, the cumulative adjustment of the power ratio The calculation formula is:

[0121]

[0122] In the formula: To adjust the maximum capacity ratio; For the system in t The regulating capacity at any given time, in kW; The reference power of the system at any given moment when it is not adjusted, in kW; The system's cumulative power consumption ratio is adjusted.T System regulation period, unit: h.

[0123] 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 / energy release period and power distribution, dynamic power distribution and operation timing optimization method between main machine direct supply and energy storage device, and comprises optimal matching scheduling based on main machine COP curve and load rate, 1, main machine preferentially operates at high load rate; 2, main machine preferentially stores energy at high time, ensures that the main machine operates at high COP, and avoids energy storage operation under low energy efficiency condition. 8760 hours of hourly building cold and heat load prediction and meteorological data input are adopted to establish a scheduling optimization model with annual hourly load prediction and meteorological conditions as input, and the constraint conditions comprise main machine output power constraint, energy storage capacity constraint and load balance constraint.

[0124] 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.

[0125] Embodiment 2

[0126] On the basis of embodiment 1, the embodiment provides a specific application example.

[0127] 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 release of the system and the hourly power are shown in the table. Figure 3 、 Figure 4 On May 1, the air conditioner cold load is small, the daily cumulative cold load is less than the rated energy of the energy storage device, the energy storage device is not full, the energy storage time is 5.1h, and the whole day cold load 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 cold load is moderate, the energy storage device is full, the energy storage time is 5.5h, and the building cold load is borne by the energy storage device and the cooling main machine on that day; the transferred electricity of the building use period is 2827 kW·h, and the transfer rate is 52%. On July 20, the air conditioner cold load is large, the energy storage device is full, the energy storage time is 5.5h, and the building cold load is borne by the energy storage device and the cooling main machine on that day; the transferred electricity of the building use period is 3315 kW·h, and the transfer rate is 40%.

[0128] 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 and hourly power are shown in Figure 5 、 Figure 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 air source heat pump; the building use period shifts 979 kW·h of electricity, 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 air source heat pump; the building use period shifts 973 kW·h of electricity, 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 is supplemented in the flat period to meet the demand of continuous high load during the day, so the building heat load is borne by the energy storage device and air source heat pump, and the energy storage device does not release heat in the flat period; the building use period shifts 1519 kW·h of electricity, and the transfer rate is 37%.

[0129] 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%.

[0130] The flexibility effect of the energy storage system in the peak period of the whole year is shown in Table 1:

[0131] Table 1 Flexibility effect of energy storage system in peak period of the whole year

[0132]

[0133] Example 3

[0134] An energy storage dynamic optimization scheduling system of a power system, which adopts the energy storage dynamic optimization scheduling method of the power system as described in any one of the embodiments 1 or 2.

[0135] Example 4

[0136] 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 the energy storage dynamic optimization scheduling method of the power system as described in the embodiments 1 or 2 when the computer program runs.

[0137] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. 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 conditioner host rated operating capacity w , maximum energy storage capacity of energy storage device X max , valley hours of energy storage period T 1, flat hours T 2, cumulative load of valley period exceeding host capacity Y 1, cumulative load of flat period exceeding host capacity Y 2, cumulative load of peak period Z 1, cumulative load of flat period Z2 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; The energy consumption model of the chiller 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; 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 energy storage dynamic optimization scheduling method of the power system according to claim 1, characterized in that, The energy consumption model of the air source heat pump 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 energy consumption model of the water pump 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.

3. The method of claim 2, wherein, The energy consumption model of the cooling tower 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 main machine rated operating capacity of the air conditioner, unit: kW; is the energy storage hours of the day, unit: h; is the maximum energy storage of the energy storage device, unit: kW·h; is i is the direct energy supply of the main machine 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 parameter includes cooling water temperature or outdoor dry bulb temperature.

9. An energy storage dynamic optimal dispatch 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.

10. A computer readable storage medium, characterized in that, A computer readable storage medium storing a computer program, wherein when the computer program is executed, the device where the computer readable storage medium is located is controlled to perform the energy storage dynamic optimization scheduling method of the power system according to any one of claims 1-8.

Images